Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.861179 DF = 1 p-value = 0.1724888 D = -7.755187 f = 0.06530398
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.959251 DF = 1 p-value = 0.1615934 D = -7.641968 f = 0.0700186
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.533039 DF = 1 p-value = 0.2156569 D = -7.05428 f = 0.05975454
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.607893 DF = 1 p-value = 0.2047883 D = -6.937927 f = 0.06398063
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.4223818 DF = 1 p-value = 0.515751 D = 1.580561 f = -0.04168209
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.126084 DF = 1 p-value = 0.7225273 D = 1.020975 f = -0.03049596
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.3821579 DF = 1 p-value = 0.5364506 D = 1.516736 f = -0.04046436
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.1021628 DF = 1 p-value = 0.7492495 D = 0.9589041 f = -0.02902156
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 1.030307 DF = 2 p-value = 0.597409 D = NA f = 0.04026791
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.889861 DF = 2 p-value = 0.6408688 D = NA f = 0.02237306
##Tabulating the distribution of gender, age among individuals of the different g6pd_202_rtpcr, thal and sickle genotypes before removal of sicklers and outliers
##Tabulating the distribution of individuals with different g6pd_202_rtpcr, thal and sickle genotypes among those with CBC data before removal of sicklers and outliers
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.4223818 DF = 1 p-value = 0.515751 D = 1.580561 f = -0.04168209
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.126084 DF = 1 p-value = 0.7225273 D = 1.020975 f = -0.03049596
##unadjusted analysis of g6pd202 on cbc indices before removal of those with incomplete genotypes {.tabset}
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.425149 DF = 1 p-value = 0.2325578 D = -6.793592 f = 0.05791955
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.49419 DF = 1 p-value = 0.2215675 D = -6.678899 f = 0.06202607
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.425149 DF = 1 p-value = 0.2325578 D = -6.793592 f = 0.05791955
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.49419 DF = 1 p-value = 0.2215675 D = -6.678899 f = 0.06202607
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.7459716 DF = 2 p-value = 0.688675 D = NA f = 0.03806457
Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.6154264 DF = 2 p-value = 0.7351261 D = NA f = 0.01997133
##Checking normality and equal variance of the phenotypes (non-transformed & transformed) in all and in malaria negative individuals
##Tabulating the distribution of gender, age among individuals of the different g6pd_202_rtpcr, thal and sickle genotypes after removal of sicklers
##Tabulating the distribution of individuals with different g6pd_202_rtpcr, thal and sickle genotypes among those with CBC data after removal of sicklers
Results of Hypothesis Test
Alternative Hypothesis:
Test Name: Shapiro-Wilk normality test
Data: pgd_genopheno_01042018$u_ghb3
Test Statistic: W = 0.9864331
P-value: 0.0006673935
| sex |
|
|
|
|
< 0.001 |
| FEMALE |
146 (62.66%) |
75 (32.19%) |
12 (5.15%) |
233 (100.00%) |
|
| MALE |
185 (76.13%) |
0 (0.00%) |
58 (23.87%) |
243 (100.00%) |
|
| malaria_status |
|
|
|
|
0.916 |
| no_malaria |
264 (69.84%) |
58 (15.34%) |
56 (14.81%) |
378 (100.00%) |
|
| assymptomatic_malaria |
40 (68.97%) |
11 (18.97%) |
7 (12.07%) |
58 (100.00%) |
|
| uncomplicated_malaria |
27 (67.50%) |
6 (15.00%) |
7 (17.50%) |
40 (100.00%) |
|
| rbc_2010 |
|
|
|
|
< 0.001 |
| meanCI |
4.73 (4.67, 4.79) |
4.52 (4.42, 4.62) |
4.42 (4.31, 4.53) |
4.65 (4.60, 4.70) |
|
| age_at_collection_years_2010 |
|
|
|
|
0.320 |
| Median |
7.81 |
7.07 |
8.18 |
7.82 |
|
| IQR |
4.68 |
4.92 |
4.41 |
4.78 |
|
| Range |
0.73 - 13.71 |
1.02 - 14.01 |
1.32 - 13.65 |
0.73 - 14.01 |
|
| Median (MAD) |
7.81 (2.39) |
7.07 (2.50) |
8.18 (2.41) |
7.82 (2.42) |
|
| Median (Q1, Q3) |
7.81 (5.35, 10.03) |
7.07 (4.81, 9.73) |
8.18 (6.36, 10.77) |
7.82 (5.34, 10.12) |
|
| hgb_2010 |
|
|
|
|
0.706 |
| Median |
11.20 |
11.40 |
11.30 |
11.30 |
|
| IQR |
1.50 |
1.45 |
1.38 |
1.50 |
|
| Range |
8.20 - 14.10 |
8.20 - 13.40 |
9.20 - 14.10 |
8.20 - 14.10 |
|
| Median (MAD) |
11.20 (0.80) |
11.40 (0.60) |
11.30 (0.70) |
11.30 (0.70) |
|
| Median (Q1, Q3) |
11.20 (10.50, 12.00) |
11.40 (10.55, 12.00) |
11.30 (10.70, 12.08) |
11.30 (10.50, 12.00) |
|
| mcv_2010 |
|
|
|
|
< 0.001 |
| Median |
73.00 |
77.00 |
80.00 |
75.00 |
|
| IQR |
12.00 |
10.00 |
6.75 |
11.00 |
|
| Range |
50.00 - 97.00 |
58.00 - 87.20 |
56.60 - 92.00 |
50.00 - 97.00 |
|
| Median (MAD) |
73.00 (6.00) |
77.00 (5.00) |
80.00 (3.50) |
75.00 (6.00) |
|
| Median (Q1, Q3) |
73.00 (66.00, 78.00) |
77.00 (70.50, 80.50) |
80.00 (77.00, 83.75) |
75.00 (68.00, 79.00) |
|
| mch_2010 |
|
|
|
|
< 0.001 |
| Median |
24.60 |
25.20 |
26.60 |
24.80 |
|
| IQR |
4.50 |
4.00 |
3.07 |
4.73 |
|
| Range |
15.30 - 35.00 |
17.80 - 30.50 |
17.30 - 30.80 |
15.30 - 35.00 |
|
| Median (MAD) |
24.60 (2.30) |
25.20 (2.00) |
26.60 (1.35) |
24.80 (2.20) |
|
| Median (Q1, Q3) |
24.60 (21.60, 26.10) |
25.20 (23.05, 27.05) |
26.60 (24.83, 27.90) |
24.80 (21.90, 26.63) |
|
| mchc_2010 |
|
|
|
|
0.491 |
| Median |
33.30 |
33.00 |
33.10 |
33.20 |
|
| IQR |
1.40 |
1.35 |
1.37 |
1.50 |
|
| Range |
29.20 - 36.60 |
30.40 - 35.00 |
30.60 - 35.20 |
29.20 - 36.60 |
|
| Median (MAD) |
33.30 (0.70) |
33.00 (0.70) |
33.10 (0.70) |
33.20 (0.70) |
|
| Median (Q1, Q3) |
33.30 (32.50, 33.90) |
33.00 (32.45, 33.80) |
33.10 (32.40, 33.77) |
33.20 (32.40, 33.90) |
|
| u_rcc |
|
|
|
|
< 0.001 |
| N-Miss |
29 |
6 |
28 |
63 |
|
| Median |
168.30 |
124.77 |
18.44 |
155.00 |
|
| IQR |
69.88 |
71.91 |
25.01 |
79.96 |
|
| Range |
0.00 - 395.31 |
9.52 - 323.70 |
0.00 - 230.94 |
0.00 - 395.31 |
|
| Median (MAD) |
168.30 (32.92) |
124.77 (35.23) |
18.44 (12.68) |
155.00 (39.89) |
|
| Median (Q1, Q3) |
168.30 (139.55, 209.43) |
124.77 (90.07, 161.99) |
18.44 (9.59, 34.60) |
155.00 (119.66, 199.62) |
|
| u_ghb3 |
|
|
|
|
< 0.001 |
| N-Miss |
29 |
6 |
28 |
63 |
|
| Median |
7.14 |
4.81 |
0.66 |
6.46 |
|
| IQR |
3.34 |
3.32 |
1.27 |
3.57 |
|
| Range |
0.00 - 17.50 |
0.33 - 12.80 |
0.00 - 8.18 |
0.00 - 17.50 |
|
| Median (MAD) |
7.14 (1.57) |
4.81 (1.44) |
0.66 (0.44) |
6.46 (1.86) |
|
| Median (Q1, Q3) |
7.14 (5.68, 9.02) |
4.81 (3.52, 6.84) |
0.66 (0.36, 1.63) |
6.46 (4.81, 8.38) |
|
| sex |
|
|
|
0.652 |
| FEMALE |
193 (82.83%) |
40 (17.17%) |
233 (100.00%) |
|
| MALE |
205 (84.36%) |
38 (15.64%) |
243 (100.00%) |
|
| malaria_status |
|
|
|
0.283 |
| no_malaria |
318 (84.13%) |
60 (15.87%) |
378 (100.00%) |
|
| assymptomatic_malaria |
50 (86.21%) |
8 (13.79%) |
58 (100.00%) |
|
| uncomplicated_malaria |
30 (75.00%) |
10 (25.00%) |
40 (100.00%) |
|
| rbc_2010 |
|
|
|
0.439 |
| meanCI |
4.64 (4.59, 4.70) |
4.69 (4.57, 4.81) |
4.65 (4.60, 4.70) |
|
| age_at_collection_years_2010 |
|
|
|
0.933 |
| Median |
7.83 |
7.65 |
7.82 |
|
| IQR |
4.81 |
4.74 |
4.78 |
|
| Range |
0.73 - 14.01 |
0.79 - 13.49 |
0.73 - 14.01 |
|
| Median (MAD) |
7.83 (2.41) |
7.65 (2.40) |
7.82 (2.42) |
|
| Median (Q1, Q3) |
7.83 (5.23, 10.04) |
7.65 (5.49, 10.23) |
7.82 (5.34, 10.12) |
|
| hgb_2010 |
|
|
|
0.634 |
| Median |
11.30 |
11.25 |
11.30 |
|
| IQR |
1.47 |
1.50 |
1.50 |
|
| Range |
8.20 - 14.10 |
8.20 - 14.00 |
8.20 - 14.10 |
|
| Median (MAD) |
11.30 (0.70) |
11.25 (0.75) |
11.30 (0.70) |
|
| Median (Q1, Q3) |
11.30 (10.53, 12.00) |
11.25 (10.50, 12.00) |
11.30 (10.50, 12.00) |
|
| mcv_2010 |
|
|
|
0.407 |
| Median |
75.00 |
74.00 |
75.00 |
|
| IQR |
11.00 |
10.75 |
11.00 |
|
| Range |
50.00 - 97.00 |
53.00 - 92.00 |
50.00 - 97.00 |
|
| Median (MAD) |
75.00 (6.00) |
74.00 (6.00) |
75.00 (6.00) |
|
| Median (Q1, Q3) |
75.00 (68.00, 79.00) |
74.00 (67.25, 78.00) |
75.00 (68.00, 79.00) |
|
| mch_2010 |
|
|
|
0.195 |
| Median |
24.95 |
24.50 |
24.80 |
|
| IQR |
4.77 |
4.43 |
4.73 |
|
| Range |
15.30 - 35.00 |
16.10 - 32.00 |
15.30 - 35.00 |
|
| Median (MAD) |
24.95 (2.25) |
24.50 (2.10) |
24.80 (2.20) |
|
| Median (Q1, Q3) |
24.95 (22.00, 26.77) |
24.50 (21.82, 26.25) |
24.80 (21.90, 26.63) |
|
| mchc_2010 |
|
|
|
0.057 |
| Median |
33.25 |
33.05 |
33.20 |
|
| IQR |
1.40 |
1.27 |
1.50 |
|
| Range |
29.20 - 36.60 |
30.40 - 35.00 |
29.20 - 36.60 |
|
| Median (MAD) |
33.25 (0.75) |
33.05 (0.65) |
33.20 (0.70) |
|
| Median (Q1, Q3) |
33.25 (32.50, 33.90) |
33.05 (32.30, 33.57) |
33.20 (32.40, 33.90) |
|
| u_rcc |
|
|
|
0.394 |
| N-Miss |
44 |
19 |
63 |
|
| Median |
154.76 |
156.50 |
155.00 |
|
| IQR |
75.95 |
81.03 |
79.96 |
|
| Range |
0.00 - 395.31 |
3.40 - 379.91 |
0.00 - 395.31 |
|
| Median (MAD) |
154.76 (37.28) |
156.50 (51.37) |
155.00 (39.89) |
|
| Median (Q1, Q3) |
154.76 (118.99, 194.94) |
156.50 (130.15, 211.18) |
155.00 (119.66, 199.62) |
|
| u_ghb3 |
|
|
|
0.351 |
| N-Miss |
44 |
19 |
63 |
|
| Median |
6.36 |
7.04 |
6.46 |
|
| IQR |
3.60 |
3.96 |
3.57 |
|
| Range |
0.00 - 17.50 |
0.14 - 16.17 |
0.00 - 17.50 |
|
| Median (MAD) |
6.36 (1.84) |
7.04 (2.11) |
6.46 (1.86) |
|
| Median (Q1, Q3) |
6.36 (4.75, 8.35) |
7.04 (5.08, 9.04) |
6.46 (4.81, 8.38) |
|
| sex |
|
|
|
|
0.682 |
| FEMALE |
72 (30.90%) |
112 (48.07%) |
49 (21.03%) |
233 (100.00%) |
|
| MALE |
84 (34.57%) |
109 (44.86%) |
50 (20.58%) |
243 (100.00%) |
|
| malaria_status |
|
|
|
|
0.775 |
| no_malaria |
125 (33.07%) |
174 (46.03%) |
79 (20.90%) |
378 (100.00%) |
|
| assymptomatic_malaria |
16 (27.59%) |
28 (48.28%) |
14 (24.14%) |
58 (100.00%) |
|
| uncomplicated_malaria |
15 (37.50%) |
19 (47.50%) |
6 (15.00%) |
40 (100.00%) |
|
| rbc_2010 |
|
|
|
|
< 0.001 |
| meanCI |
4.41 (4.34, 4.49) |
4.61 (4.54, 4.67) |
5.12 (5.04, 5.20) |
4.65 (4.60, 4.70) |
|
| age_at_collection_years_2010 |
|
|
|
|
0.334 |
| Median |
7.39 |
7.81 |
8.66 |
7.82 |
|
| IQR |
5.15 |
5.30 |
4.27 |
4.78 |
|
| Range |
0.73 - 13.65 |
0.80 - 14.01 |
0.73 - 13.54 |
0.73 - 14.01 |
|
| Median (MAD) |
7.39 (2.59) |
7.81 (2.71) |
8.66 (1.98) |
7.82 (2.42) |
|
| Median (Q1, Q3) |
7.39 (4.72, 9.87) |
7.81 (5.23, 10.53) |
8.66 (5.67, 9.94) |
7.82 (5.34, 10.12) |
|
| hgb_2010 |
|
|
|
|
< 0.001 |
| Median |
11.50 |
11.30 |
11.00 |
11.30 |
|
| IQR |
1.40 |
1.50 |
1.15 |
1.50 |
|
| Range |
8.20 - 14.10 |
8.20 - 14.10 |
8.40 - 13.30 |
8.20 - 14.10 |
|
| Median (MAD) |
11.50 (0.70) |
11.30 (0.70) |
11.00 (0.70) |
11.30 (0.70) |
|
| Median (Q1, Q3) |
11.50 (10.80, 12.20) |
11.30 (10.50, 12.00) |
11.00 (10.25, 11.40) |
11.30 (10.50, 12.00) |
|
| mcv_2010 |
|
|
|
|
< 0.001 |
| Median |
79.00 |
75.00 |
66.00 |
75.00 |
|
| IQR |
8.00 |
7.00 |
5.00 |
11.00 |
|
| Range |
51.00 - 97.00 |
54.00 - 90.00 |
50.00 - 82.00 |
50.00 - 97.00 |
|
| Median (MAD) |
79.00 (4.00) |
75.00 (3.00) |
66.00 (3.00) |
75.00 (6.00) |
|
| Median (Q1, Q3) |
79.00 (75.00, 83.00) |
75.00 (71.00, 78.00) |
66.00 (63.00, 68.00) |
75.00 (68.00, 79.00) |
|
| mch_2010 |
|
|
|
|
< 0.001 |
| Median |
26.80 |
24.90 |
21.30 |
24.80 |
|
| IQR |
3.02 |
2.60 |
1.40 |
4.73 |
|
| Range |
15.80 - 35.00 |
16.50 - 30.00 |
15.30 - 28.10 |
15.30 - 35.00 |
|
| Median (MAD) |
26.80 (1.50) |
24.90 (1.30) |
21.30 (0.80) |
24.80 (2.20) |
|
| Median (Q1, Q3) |
26.80 (25.27, 28.30) |
24.90 (23.60, 26.20) |
21.30 (20.50, 21.90) |
24.80 (21.90, 26.63) |
|
| mchc_2010 |
|
|
|
|
< 0.001 |
| Median |
33.60 |
33.20 |
32.10 |
33.20 |
|
| IQR |
1.10 |
1.20 |
1.15 |
1.50 |
|
| Range |
30.40 - 36.60 |
29.20 - 35.30 |
30.40 - 34.40 |
29.20 - 36.60 |
|
| Median (MAD) |
33.60 (0.60) |
33.20 (0.60) |
32.10 (0.60) |
33.20 (0.70) |
|
| Median (Q1, Q3) |
33.60 (33.10, 34.20) |
33.20 (32.60, 33.80) |
32.10 (31.75, 32.90) |
33.20 (32.40, 33.90) |
|
| u_rcc |
|
|
|
|
0.757 |
| N-Miss |
21 |
30 |
12 |
63 |
|
| Median |
151.70 |
155.80 |
155.24 |
155.00 |
|
| IQR |
81.24 |
74.01 |
85.98 |
79.96 |
|
| Range |
0.00 - 394.00 |
0.00 - 395.31 |
0.00 - 292.58 |
0.00 - 395.31 |
|
| Median (MAD) |
151.70 (38.01) |
155.80 (37.33) |
155.24 (44.93) |
155.00 (39.89) |
|
| Median (Q1, Q3) |
151.70 (121.60, 202.84) |
155.80 (117.67, 191.68) |
155.24 (117.36, 203.33) |
155.00 (119.66, 199.62) |
|
| u_ghb3 |
|
|
|
|
0.014 |
| N-Miss |
21 |
30 |
12 |
63 |
|
| Median |
5.96 |
6.32 |
7.31 |
6.46 |
|
| IQR |
3.06 |
3.69 |
4.50 |
3.57 |
|
| Range |
0.00 - 16.17 |
0.00 - 17.50 |
0.00 - 15.33 |
0.00 - 17.50 |
|
| Median (MAD) |
5.96 (1.54) |
6.32 (1.97) |
7.31 (2.32) |
6.46 (1.86) |
|
| Median (Q1, Q3) |
5.96 (4.75, 7.81) |
6.32 (4.72, 8.42) |
7.31 (5.19, 9.70) |
6.46 (4.81, 8.38) |
|
| sex |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| FEMALE |
44 (18.88%) |
68 (29.18%) |
34 (14.59%) |
23 (9.87%) |
40 (17.17%) |
12 (5.15%) |
5 (2.15%) |
4 (1.72%) |
3 (1.29%) |
233 (100.00%) |
|
| MALE |
67 (27.57%) |
75 (30.86%) |
43 (17.70%) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
17 (7.00%) |
34 (13.99%) |
7 (2.88%) |
243 (100.00%) |
|
| malaria_status |
|
|
|
|
|
|
|
|
|
|
0.9231 |
| no_malaria |
88 (23.28%) |
116 (30.69%) |
60 (15.87%) |
18 (4.76%) |
29 (7.67%) |
11 (2.91%) |
19 (5.03%) |
29 (7.67%) |
8 (2.12%) |
378 (100.00%) |
|
| assymptomatic_malaria |
12 (20.69%) |
15 (25.86%) |
13 (22.41%) |
3 (5.17%) |
8 (13.79%) |
0 (0.00%) |
1 (1.72%) |
5 (8.62%) |
1 (1.72%) |
58 (100.00%) |
|
| uncomplicated_malaria |
11 (27.50%) |
12 (30.00%) |
4 (10.00%) |
2 (5.00%) |
3 (7.50%) |
1 (2.50%) |
2 (5.00%) |
4 (10.00%) |
1 (2.50%) |
40 (100.00%) |
|
| rbc_2010 |
|
|
|
|
|
|
|
|
|
|
< 0.0012 |
| meanCI |
4.45 (4.35, 4.54) |
4.72 (4.64, 4.81) |
5.15 (5.06, 5.24) |
4.37 (4.19, 4.55) |
4.44 (4.33, 4.55) |
5.07 (4.79, 5.35) |
4.30 (4.08, 4.51) |
4.35 (4.22, 4.49) |
4.94 (4.73, 5.15) |
4.65 (4.60, 4.70) |
|
- Pearson’s Chi-squared test
- Linear Model ANOVA
| age_at_collection_years_2010 |
|
|
|
|
|
|
|
|
|
|
0.3261 |
| Median |
7.59 |
7.64 |
8.75 |
7.07 |
6.26 |
9.30 |
7.23 |
9.06 |
7.51 |
7.82 |
|
| IQR |
5.29 |
4.94 |
3.92 |
4.24 |
5.30 |
2.70 |
4.67 |
4.29 |
1.62 |
4.78 |
|
| Range |
0.73 - 13.49 |
0.80 - 13.71 |
0.73 - 13.54 |
1.02 - 11.53 |
1.05 - 14.01 |
4.05 - 12.39 |
1.51 - 13.65 |
1.32 - 13.09 |
4.01 - 10.64 |
0.73 - 14.01 |
|
| Median (MAD) |
7.59 (2.45) |
7.64 (2.53) |
8.75 (2.27) |
7.07 (2.40) |
6.26 (2.92) |
9.30 (1.48) |
7.23 (2.44) |
9.06 (2.13) |
7.51 (0.97) |
7.82 (2.42) |
|
| Median (Q1, Q3) |
7.59 (4.65, 9.94) |
7.64 (5.35, 10.29) |
8.75 (5.55, 9.48) |
7.07 (4.68, 8.92) |
6.26 (4.65, 9.95) |
9.30 (7.80, 10.50) |
7.23 (5.95, 10.62) |
9.06 (6.89, 11.19) |
7.51 (6.75, 8.38) |
7.82 (5.34, 10.12) |
|
| hgb_2010 |
|
|
|
|
|
|
|
|
|
|
0.0031 |
| Median |
11.40 |
11.30 |
10.80 |
11.60 |
11.30 |
11.35 |
11.50 |
11.30 |
10.40 |
11.30 |
|
| IQR |
1.50 |
1.50 |
1.20 |
1.20 |
1.43 |
0.77 |
1.15 |
1.40 |
0.98 |
1.50 |
|
| Range |
8.20 - 14.10 |
8.50 - 13.80 |
8.50 - 13.30 |
9.70 - 13.40 |
8.20 - 13.20 |
8.40 - 12.10 |
10.00 - 14.10 |
9.20 - 14.10 |
9.80 - 12.10 |
8.20 - 14.10 |
|
| Median (MAD) |
11.40 (0.80) |
11.30 (0.80) |
10.80 (0.60) |
11.60 (0.70) |
11.30 (0.75) |
11.35 (0.35) |
11.50 (0.65) |
11.30 (0.70) |
10.40 (0.55) |
11.30 (0.70) |
|
| Median (Q1, Q3) |
11.40 (10.70, 12.20) |
11.30 (10.50, 12.00) |
10.80 (10.20, 11.40) |
11.60 (10.95, 12.15) |
11.30 (10.35, 11.77) |
11.35 (10.92, 11.70) |
11.50 (11.03, 12.17) |
11.30 (10.83, 12.23) |
10.40 (10.08, 11.05) |
11.30 (10.50, 12.00) |
|
| mcv_2010 |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| Median |
79.00 |
73.00 |
65.00 |
81.00 |
76.65 |
68.85 |
83.50 |
80.00 |
67.50 |
75.00 |
|
| IQR |
7.65 |
6.00 |
5.00 |
8.50 |
6.25 |
3.00 |
8.75 |
3.95 |
3.75 |
11.00 |
|
| Range |
51.00 - 97.00 |
54.00 - 85.00 |
50.00 - 82.00 |
59.00 - 87.20 |
58.00 - 83.70 |
61.00 - 79.00 |
56.60 - 92.00 |
63.00 - 90.00 |
65.00 - 73.00 |
50.00 - 97.00 |
|
| Median (MAD) |
79.00 (4.00) |
73.00 (3.00) |
65.00 (3.00) |
81.00 (4.00) |
76.65 (3.35) |
68.85 (1.00) |
83.50 (4.35) |
80.00 (2.00) |
67.50 (1.50) |
75.00 (6.00) |
|
| Median (Q1, Q3) |
79.00 (74.50, 82.15) |
73.00 (70.00, 76.00) |
65.00 (62.00, 67.00) |
81.00 (75.50, 84.00) |
76.65 (72.75, 79.00) |
68.85 (66.00, 69.00) |
83.50 (78.00, 86.75) |
80.00 (78.00, 81.95) |
67.50 (66.00, 69.75) |
75.00 (68.00, 79.00) |
|
| mch_2010 |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| Median |
26.60 |
24.60 |
21.10 |
27.20 |
25.05 |
21.80 |
28.10 |
26.60 |
21.30 |
24.80 |
|
| IQR |
3.25 |
2.55 |
1.50 |
3.10 |
2.45 |
1.12 |
3.07 |
2.22 |
1.47 |
4.73 |
|
| Range |
15.80 - 35.00 |
16.50 - 28.60 |
15.30 - 28.10 |
19.90 - 30.50 |
17.80 - 28.70 |
18.80 - 27.20 |
17.30 - 30.80 |
19.60 - 30.00 |
20.10 - 22.80 |
15.30 - 35.00 |
|
| Median (MAD) |
26.60 (1.60) |
24.60 (1.00) |
21.10 (0.70) |
27.20 (1.40) |
25.05 (1.55) |
21.80 (0.65) |
28.10 (1.75) |
26.60 (1.20) |
21.30 (0.55) |
24.80 (2.20) |
|
| Median (Q1, Q3) |
26.60 (24.85, 28.10) |
24.60 (22.95, 25.50) |
21.10 (20.30, 21.80) |
27.20 (25.25, 28.35) |
25.05 (24.17, 26.63) |
21.80 (21.32, 22.45) |
28.10 (26.15, 29.22) |
26.60 (25.50, 27.72) |
21.30 (21.00, 22.47) |
24.80 (21.90, 26.63) |
|
| mchc_2010 |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| Median |
33.60 |
33.30 |
32.40 |
33.70 |
32.90 |
31.95 |
33.50 |
33.15 |
31.55 |
33.20 |
|
| IQR |
1.10 |
1.20 |
1.20 |
0.90 |
1.17 |
1.05 |
1.15 |
1.25 |
1.05 |
1.50 |
|
| Range |
30.40 - 36.60 |
29.20 - 35.30 |
30.60 - 34.30 |
32.30 - 35.00 |
30.40 - 34.90 |
30.40 - 34.40 |
30.70 - 34.50 |
31.20 - 35.20 |
30.60 - 32.90 |
29.20 - 36.60 |
|
| Median (MAD) |
33.60 (0.60) |
33.30 (0.60) |
32.40 (0.60) |
33.70 (0.40) |
32.90 (0.60) |
31.95 (0.45) |
33.50 (0.60) |
33.15 (0.65) |
31.55 (0.55) |
33.20 (0.70) |
|
| Median (Q1, Q3) |
33.60 (33.15, 34.25) |
33.30 (32.70, 33.90) |
32.40 (31.90, 33.10) |
33.70 (33.20, 34.10) |
32.90 (32.48, 33.65) |
31.95 (31.68, 32.73) |
33.50 (33.02, 34.18) |
33.15 (32.52, 33.77) |
31.55 (30.95, 32.00) |
33.20 (32.40, 33.90) |
|
| u_rcc |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| N-Miss |
11 |
10 |
8 |
0 |
4 |
2 |
10 |
16 |
2 |
63 |
|
| Median |
171.41 |
166.41 |
168.53 |
140.72 |
126.61 |
96.28 |
14.41 |
22.61 |
10.27 |
155.00 |
|
| IQR |
75.12 |
60.00 |
69.53 |
86.30 |
69.43 |
65.78 |
74.72 |
19.52 |
33.45 |
79.96 |
|
| Range |
59.35 - 394.00 |
0.00 - 395.31 |
54.19 - 292.58 |
9.52 - 323.70 |
71.27 - 267.95 |
60.10 - 195.37 |
0.00 - 228.30 |
0.00 - 230.94 |
0.00 - 175.70 |
0.00 - 395.31 |
|
| Median (MAD) |
171.41 (35.60) |
166.41 (29.72) |
168.53 (37.89) |
140.72 (49.82) |
126.61 (34.18) |
96.28 (33.35) |
14.41 (12.71) |
22.61 (9.96) |
10.27 (7.90) |
155.00 (39.89) |
|
| Median (Q1, Q3) |
171.41 (137.66, 212.78) |
166.41 (144.84, 204.84) |
168.53 (140.11, 209.64) |
140.72 (77.41, 163.70) |
126.61 (96.43, 165.86) |
96.28 (66.10, 131.88) |
14.41 (5.59, 80.32) |
22.61 (12.93, 32.44) |
10.27 (4.99, 38.43) |
155.00 (119.66, 199.62) |
|
| u_ghb3 |
|
|
|
|
|
|
|
|
|
|
< 0.0011 |
| N-Miss |
11 |
10 |
8 |
0 |
4 |
2 |
10 |
16 |
2 |
63 |
|
| Median |
6.57 |
7.13 |
7.96 |
4.93 |
4.91 |
4.34 |
0.51 |
0.84 |
0.47 |
6.46 |
|
| IQR |
2.67 |
3.16 |
3.49 |
3.38 |
3.28 |
2.69 |
2.42 |
0.74 |
1.56 |
3.57 |
|
| Range |
2.19 - 16.17 |
0.00 - 17.50 |
2.89 - 15.33 |
0.33 - 12.80 |
2.86 - 10.05 |
2.66 - 9.00 |
0.00 - 8.04 |
0.00 - 7.67 |
0.00 - 8.18 |
0.00 - 17.50 |
|
| Median (MAD) |
6.57 (1.37) |
7.13 (1.47) |
7.96 (1.89) |
4.93 (1.54) |
4.91 (1.50) |
4.34 (1.40) |
0.51 (0.44) |
0.84 (0.35) |
0.47 (0.36) |
6.46 (1.86) |
|
| Median (Q1, Q3) |
6.57 (5.32, 7.99) |
7.13 (5.71, 8.87) |
7.96 (6.56, 10.05) |
4.93 (2.80, 6.18) |
4.91 (3.75, 7.04) |
4.34 (3.05, 5.74) |
0.51 (0.29, 2.71) |
0.84 (0.49, 1.23) |
0.47 (0.23, 1.79) |
6.46 (4.81, 8.38) |
|
- Kruskal-Wallis rank sum test
| sex |
|
|
|
|
|
|
|
< 0.0011 |
| FEMALE |
122 (52.36%) |
24 (10.30%) |
61 (26.18%) |
14 (6.01%) |
10 (4.29%) |
2 (0.86%) |
233 (100.00%) |
|
| MALE |
157 (64.61%) |
28 (11.52%) |
0 (0.00%) |
0 (0.00%) |
48 (19.75%) |
10 (4.12%) |
243 (100.00%) |
|
| malaria_status |
|
|
|
|
|
|
|
0.4261 |
| no_malaria |
222 (58.73%) |
42 (11.11%) |
49 (12.96%) |
9 (2.38%) |
47 (12.43%) |
9 (2.38%) |
378 (100.00%) |
|
| assymptomatic_malaria |
34 (58.62%) |
6 (10.34%) |
9 (15.52%) |
2 (3.45%) |
7 (12.07%) |
0 (0.00%) |
58 (100.00%) |
|
| uncomplicated_malaria |
23 (57.50%) |
4 (10.00%) |
3 (7.50%) |
3 (7.50%) |
4 (10.00%) |
3 (7.50%) |
40 (100.00%) |
|
| rbc_2010 |
|
|
|
|
|
|
|
< 0.0012 |
| meanCI |
4.72 (4.66, 4.78) |
4.78 (4.63, 4.93) |
4.55 (4.43, 4.66) |
4.40 (4.12, 4.68) |
4.37 (4.25, 4.49) |
4.66 (4.37, 4.96) |
4.65 (4.60, 4.70) |
|
- Pearson’s Chi-squared test
- Linear Model ANOVA
| age_at_collection_years_2010 |
|
|
|
|
|
|
|
0.4161 |
| Median |
7.77 |
8.46 |
7.82 |
5.40 |
8.18 |
7.96 |
7.82 |
|
| IQR |
4.92 |
4.30 |
4.59 |
5.15 |
4.54 |
4.54 |
4.78 |
|
| Range |
0.73 - 13.71 |
0.79 - 13.49 |
1.05 - 14.01 |
1.02 - 10.88 |
1.32 - 13.65 |
2.81 - 12.76 |
0.73 - 14.01 |
|
| Median (MAD) |
7.77 (2.47) |
8.46 (2.37) |
7.82 (2.39) |
5.40 (2.04) |
8.18 (2.34) |
7.96 (2.74) |
7.82 (2.42) |
|
| Median (Q1, Q3) |
7.77 (5.04, 9.96) |
8.46 (6.08, 10.38) |
7.82 (5.25, 9.84) |
5.40 (3.84, 8.98) |
8.18 (6.37, 10.91) |
7.96 (6.13, 10.67) |
7.82 (5.34, 10.12) |
|
| hgb_2010 |
|
|
|
|
|
|
|
0.4641 |
| Median |
11.20 |
11.30 |
11.40 |
10.65 |
11.25 |
11.35 |
11.30 |
|
| IQR |
1.50 |
1.53 |
1.10 |
1.42 |
1.55 |
0.52 |
1.50 |
|
| Range |
8.20 - 14.10 |
8.40 - 14.00 |
8.30 - 13.40 |
8.20 - 12.30 |
9.20 - 14.10 |
10.10 - 13.10 |
8.20 - 14.10 |
|
| Median (MAD) |
11.20 (0.80) |
11.30 (0.80) |
11.40 (0.60) |
10.65 (0.85) |
11.25 (0.80) |
11.35 (0.30) |
11.30 (0.70) |
|
| Median (Q1, Q3) |
11.20 (10.50, 12.00) |
11.30 (10.50, 12.03) |
11.40 (10.90, 12.00) |
10.65 (10.22, 11.65) |
11.25 (10.63, 12.17) |
11.35 (11.03, 11.55) |
11.30 (10.50, 12.00) |
|
| mcv_2010 |
|
|
|
|
|
|
|
< 0.0011 |
| Median |
73.00 |
72.00 |
77.00 |
76.00 |
81.00 |
77.00 |
75.00 |
|
| IQR |
12.00 |
10.25 |
9.00 |
10.75 |
7.75 |
6.00 |
11.00 |
|
| Range |
50.00 - 97.00 |
53.00 - 92.00 |
58.00 - 87.20 |
59.00 - 85.00 |
56.60 - 92.00 |
66.00 - 81.00 |
50.00 - 97.00 |
|
| Median (MAD) |
73.00 (6.00) |
72.00 (5.50) |
77.00 (5.00) |
76.00 (5.50) |
81.00 (4.00) |
77.00 (3.00) |
75.00 (6.00) |
|
| Median (Q1, Q3) |
73.00 (66.00, 78.00) |
72.00 (66.00, 76.25) |
77.00 (71.00, 80.00) |
76.00 (70.25, 81.00) |
81.00 (77.00, 84.75) |
77.00 (72.50, 78.50) |
75.00 (68.00, 79.00) |
|
| mch_2010 |
|
|
|
|
|
|
|
< 0.0011 |
| Median |
24.60 |
24.05 |
25.30 |
24.85 |
26.70 |
25.05 |
24.80 |
|
| IQR |
4.70 |
4.00 |
4.00 |
4.08 |
2.90 |
3.22 |
4.73 |
|
| Range |
15.30 - 35.00 |
16.10 - 32.00 |
17.80 - 30.50 |
18.40 - 28.50 |
17.30 - 30.80 |
21.00 - 26.80 |
15.30 - 35.00 |
|
| Median (MAD) |
24.60 (2.30) |
24.05 (2.10) |
25.30 (2.00) |
24.85 (2.25) |
26.70 (1.45) |
25.05 (1.45) |
24.80 (2.20) |
|
| Median (Q1, Q3) |
24.60 (21.50, 26.20) |
24.05 (21.80, 25.80) |
25.30 (23.10, 27.10) |
24.85 (22.82, 26.90) |
26.70 (25.40, 28.30) |
25.05 (23.15, 26.37) |
24.80 (21.90, 26.63) |
|
| mchc_2010 |
|
|
|
|
|
|
|
0.2511 |
| Median |
33.30 |
33.20 |
33.10 |
32.95 |
33.25 |
32.55 |
33.20 |
|
| IQR |
1.40 |
1.30 |
1.30 |
1.03 |
1.55 |
1.13 |
1.50 |
|
| Range |
29.20 - 36.60 |
30.40 - 35.00 |
30.40 - 35.00 |
30.40 - 34.30 |
30.60 - 35.20 |
31.10 - 33.90 |
29.20 - 36.60 |
|
| Median (MAD) |
33.30 (0.70) |
33.20 (0.65) |
33.10 (0.70) |
32.95 (0.50) |
33.25 (0.80) |
32.55 (0.60) |
33.20 (0.70) |
|
| Median (Q1, Q3) |
33.30 (32.50, 33.90) |
33.20 (32.40, 33.70) |
33.10 (32.50, 33.80) |
32.95 (32.37, 33.40) |
33.25 (32.40, 33.95) |
32.55 (32.07, 33.20) |
33.20 (32.40, 33.90) |
|
| u_rcc |
|
|
|
|
|
|
|
< 0.0011 |
| N-Miss |
19 |
10 |
3 |
3 |
22 |
6 |
63 |
|
| Median |
165.36 |
193.77 |
126.69 |
114.57 |
19.15 |
12.65 |
155.00 |
|
| IQR |
64.63 |
70.08 |
84.95 |
50.00 |
31.22 |
16.32 |
79.96 |
|
| Range |
0.00 - 395.31 |
80.94 - 379.91 |
9.52 - 323.70 |
38.65 - 148.08 |
0.00 - 230.94 |
3.40 - 175.70 |
0.00 - 395.31 |
|
| Median (MAD) |
165.36 (32.74) |
193.77 (38.55) |
126.69 (39.05) |
114.57 (26.15) |
19.15 (12.81) |
12.65 (7.44) |
155.00 (39.89) |
|
| Median (Q1, Q3) |
165.36 (139.03, 203.67) |
193.77 (153.76, 223.84) |
126.69 (90.95, 175.90) |
114.57 (89.63, 139.63) |
19.15 (9.95, 41.16) |
12.65 (8.37, 24.69) |
155.00 (119.66, 199.62) |
|
| u_ghb3 |
|
|
|
|
|
|
|
< 0.0011 |
| N-Miss |
19 |
10 |
3 |
3 |
22 |
6 |
63 |
|
| Median |
7.02 |
7.83 |
4.84 |
4.26 |
0.70 |
0.48 |
6.46 |
|
| IQR |
3.12 |
3.04 |
3.39 |
1.83 |
1.43 |
0.59 |
3.57 |
|
| Range |
0.00 - 17.50 |
3.10 - 16.17 |
0.33 - 12.80 |
1.56 - 7.51 |
0.00 - 8.04 |
0.14 - 8.18 |
0.00 - 17.50 |
|
| Median (MAD) |
7.02 (1.50) |
7.83 (1.64) |
4.84 (1.55) |
4.26 (1.10) |
0.70 (0.43) |
0.48 (0.27) |
6.46 (1.86) |
|
| Median (Q1, Q3) |
7.02 (5.58, 8.69) |
7.83 (6.59, 9.63) |
4.84 (3.53, 6.92) |
4.26 (3.68, 5.51) |
0.70 (0.37, 1.80) |
0.48 (0.33, 0.93) |
6.46 (4.81, 8.38) |
|
- Kruskal-Wallis rank sum test
##2010 age and CBC summary (mean, sd, median) by rbc polymorphisms CBC summary in all individuals by rbc polymorphisms
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
| g6pd_202_rtpcr |
NORM |
HET |
HOM/HEMI |
| rbc_2010_mean |
4.729728 |
4.520133 |
4.419000 |
| hgb_2010_mean |
11.23233 |
11.19333 |
11.39429 |
| mcv_2010_mean |
72.13233 |
75.20933 |
78.60857 |
| mch_2010_mean |
23.98369 |
24.91600 |
26.00143 |
| mchc_2010_mean |
33.17915 |
33.05600 |
33.01143 |
| rbc_2010_se |
0.03003609 |
0.05209883 |
0.05633608 |
| hgb_2010_se |
0.06709221 |
0.12985555 |
0.12153071 |
| mcv_2010_se |
0.4368500 |
0.8026043 |
0.8863813 |
| mch_2010_se |
0.1736594 |
0.3292527 |
0.3593616 |
| mchc_2010_se |
0.06010505 |
0.12446990 |
0.12237372 |
| rbc_2010_sd |
0.5464588 |
0.4511891 |
0.4713415 |
| hgb_2010_sd |
1.220636 |
1.124582 |
1.016799 |
| mcv_2010_sd |
7.947789 |
6.950757 |
7.415998 |
| mch_2010_sd |
3.159455 |
2.851412 |
3.006635 |
| mchc_2010_sd |
1.093515 |
1.077941 |
1.023852 |
| rbc_2010_median |
4.74 |
4.47 |
4.38 |
| hgb_2010_median |
11.2 |
11.4 |
11.3 |
| mcv_2010_median |
73 |
77 |
80 |
| mch_2010_median |
24.6 |
25.2 |
26.6 |
| mchc_2010_median |
33.3 |
33.0 |
33.1 |
| rbc_2010_gmean |
4.697179 |
4.498074 |
4.394707 |
| hgb_2010_gmean |
11.16485 |
11.13425 |
11.35008 |
| mcv_2010_gmean |
71.68336 |
74.87806 |
78.24335 |
| mch_2010_gmean |
23.76850 |
24.74600 |
25.81646 |
| mchc_2010_gmean |
33.16106 |
33.03846 |
32.99560 |
| rbc_2010_lci |
4.637095 |
4.396203 |
4.285636 |
| hgb_2010_lci |
11.03198 |
10.86773 |
11.11263 |
| mcv_2010_lci |
70.81329 |
73.24616 |
76.42225 |
| mch_2010_lci |
23.42121 |
24.07401 |
25.07062 |
| mchc_2010_lci |
33.04240 |
32.78936 |
32.75035 |
| rbc_2010_uci |
4.758042 |
4.602306 |
4.506553 |
| hgb_2010_uci |
11.29933 |
11.40729 |
11.59260 |
| mcv_2010_uci |
72.56413 |
76.54633 |
80.10784 |
| mch_2010_uci |
24.12093 |
25.43674 |
26.58449 |
| mchc_2010_uci |
33.28014 |
33.28946 |
33.24268 |
| rbc_2010_Gsd |
1.126443 |
1.104692 |
1.111155 |
| hgb_2010_Gsd |
1.117090 |
1.111044 |
1.092719 |
| mcv_2010_Gsd |
1.119567 |
1.100508 |
1.103808 |
| mch_2010_Gsd |
1.145829 |
1.127112 |
1.130824 |
| mchc_2010_Gsd |
1.033707 |
1.033442 |
1.031783 |
| rbc_2010_min |
2.92 |
3.41 |
3.62 |
| hgb_2010_min |
8.2 |
8.2 |
9.2 |
| mcv_2010_min |
50.0 |
58.0 |
56.6 |
| mch_2010_min |
15.3 |
17.8 |
17.3 |
| mchc_2010_min |
29.2 |
30.4 |
30.6 |
| rbc_2010_max |
6.14 |
5.66 |
5.84 |
| hgb_2010_max |
14.1 |
13.4 |
14.1 |
| mcv_2010_max |
97.0 |
87.2 |
92.0 |
| mch_2010_max |
35.0 |
30.5 |
30.8 |
| mchc_2010_max |
36.6 |
35.0 |
35.2 |
| rbc_2010_IQR |
0.7350001 |
0.6350000 |
0.7500001 |
| hgb_2010_IQR |
1.500 |
1.450 |
1.375 |
| mcv_2010_IQR |
12.00 |
10.00 |
6.75 |
| mch_2010_IQR |
4.500000 |
4.000000 |
3.074999 |
| mchc_2010_IQR |
1.400002 |
1.349998 |
1.374998 |
| rbc_2010_q25 |
4.375 |
4.190 |
4.005 |
| hgb_2010_q25 |
10.50 |
10.55 |
10.70 |
| mcv_2010_q25 |
66.0 |
70.5 |
77.0 |
| mch_2010_q25 |
21.600 |
23.050 |
24.825 |
| mchc_2010_q25 |
32.50 |
32.45 |
32.40 |
| rbc_2010_q75 |
5.110 |
4.825 |
4.755 |
| hgb_2010_q75 |
12.000 |
12.000 |
12.075 |
| mcv_2010_q75 |
78.00 |
80.50 |
83.75 |
| mch_2010_q75 |
26.10 |
27.05 |
27.90 |
| mchc_2010_q75 |
33.900 |
33.800 |
33.775 |
| thal |
NORM |
HET |
HOM |
| rbc_2010_mean |
4.414679 |
4.608416 |
5.118485 |
| hgb_2010_mean |
11.49423 |
11.25430 |
10.85556 |
| mcv_2010_mean |
78.03077 |
73.97466 |
65.63535 |
| mch_2010_mean |
26.26090 |
24.55611 |
21.25050 |
| mchc_2010_mean |
33.60256 |
33.15430 |
32.35556 |
| rbc_2010_se |
0.03971980 |
0.03252558 |
0.04023825 |
| hgb_2010_se |
0.09687952 |
0.07932879 |
0.10192158 |
| mcv_2010_se |
0.6434878 |
0.4324625 |
0.4972887 |
| mch_2010_se |
0.2510929 |
0.1703460 |
0.1797372 |
| mchc_2010_se |
0.07998629 |
0.06849868 |
0.08963177 |
| rbc_2010_sd |
0.4961002 |
0.4835275 |
0.4003655 |
| hgb_2010_sd |
1.210025 |
1.179307 |
1.014107 |
| mcv_2010_sd |
8.037161 |
6.429017 |
4.947960 |
| mch_2010_sd |
3.136149 |
2.532376 |
1.788362 |
| mchc_2010_sd |
0.9990284 |
1.0183060 |
0.8918248 |
| rbc_2010_median |
4.395 |
4.620 |
5.160 |
| hgb_2010_median |
11.5 |
11.3 |
11.0 |
| mcv_2010_median |
79 |
75 |
66 |
| mch_2010_median |
26.8 |
24.9 |
21.3 |
| mchc_2010_median |
33.6 |
33.2 |
32.1 |
| rbc_2010_gmean |
4.386572 |
4.583197 |
5.102486 |
| hgb_2010_gmean |
11.42877 |
11.19120 |
10.80758 |
| mcv_2010_gmean |
77.57766 |
73.68260 |
65.44965 |
| mch_2010_gmean |
26.05123 |
24.41524 |
21.17668 |
| mchc_2010_gmean |
33.58765 |
33.13849 |
32.34345 |
| rbc_2010_lci |
4.308037 |
4.519734 |
5.021449 |
| hgb_2010_lci |
11.23454 |
11.03333 |
10.60410 |
| mcv_2010_lci |
76.22501 |
72.80481 |
64.46624 |
| mch_2010_lci |
25.51541 |
24.06191 |
20.82515 |
| mchc_2010_lci |
33.42880 |
33.00221 |
32.16673 |
| rbc_2010_uci |
4.466538 |
4.647550 |
5.184831 |
| hgb_2010_uci |
11.62637 |
11.35133 |
11.01496 |
| mcv_2010_uci |
78.95431 |
74.57098 |
66.44805 |
| mch_2010_uci |
26.59831 |
24.77376 |
21.53414 |
| mchc_2010_uci |
33.74726 |
33.27533 |
32.52114 |
| rbc_2010_Gsd |
1.121005 |
1.110908 |
1.083578 |
| hgb_2010_Gsd |
1.114474 |
1.113119 |
1.099989 |
| mcv_2010_Gsd |
1.117638 |
1.094615 |
1.078862 |
| mch_2010_Gsd |
1.140428 |
1.116234 |
1.087550 |
| mchc_2010_Gsd |
1.030428 |
1.031573 |
1.027851 |
| rbc_2010_min |
2.92 |
3.36 |
4.00 |
| hgb_2010_min |
8.2 |
8.2 |
8.4 |
| mcv_2010_min |
51 |
54 |
50 |
| mch_2010_min |
15.8 |
16.5 |
15.3 |
| mchc_2010_min |
30.4 |
29.2 |
30.4 |
| rbc_2010_max |
5.86 |
6.14 |
6.04 |
| hgb_2010_max |
14.1 |
14.1 |
13.3 |
| mcv_2010_max |
97 |
90 |
82 |
| mch_2010_max |
35.0 |
30.0 |
28.1 |
| mchc_2010_max |
36.6 |
35.3 |
34.4 |
| rbc_2010_IQR |
0.6199999 |
0.5999999 |
0.5400000 |
| hgb_2010_IQR |
1.40 |
1.50 |
1.15 |
| mcv_2010_IQR |
8 |
7 |
5 |
| mch_2010_IQR |
3.025 |
2.600 |
1.400 |
| mchc_2010_IQR |
1.100002 |
1.200001 |
1.150002 |
| rbc_2010_q25 |
4.110 |
4.300 |
4.845 |
| hgb_2010_q25 |
10.80 |
10.50 |
10.25 |
| mcv_2010_q25 |
75 |
71 |
63 |
| mch_2010_q25 |
25.275 |
23.600 |
20.500 |
| mchc_2010_q25 |
33.10 |
32.60 |
31.75 |
| rbc_2010_q75 |
4.730 |
4.900 |
5.385 |
| hgb_2010_q75 |
12.2 |
12.0 |
11.4 |
| mcv_2010_q75 |
83 |
78 |
68 |
| mch_2010_q75 |
28.3 |
26.2 |
21.9 |
| mchc_2010_q75 |
34.2 |
33.8 |
32.9 |
| sickle |
NORM |
HET |
| rbc_2010_mean |
4.642588 |
4.693974 |
| hgb_2010_mean |
11.25704 |
11.21410 |
| mcv_2010_mean |
73.67764 |
73.01795 |
| mch_2010_mean |
24.49347 |
24.08974 |
| mchc_2010_mean |
33.17462 |
32.93333 |
| rbc_2010_se |
0.02684816 |
0.06048735 |
| hgb_2010_se |
0.05908877 |
0.13323412 |
| mcv_2010_se |
0.4087408 |
0.8609343 |
| mch_2010_se |
0.1608008 |
0.3364453 |
| mchc_2010_se |
0.05511182 |
0.10902753 |
| rbc_2010_sd |
0.5356190 |
0.5342098 |
| hgb_2010_sd |
1.178817 |
1.176692 |
| mcv_2010_sd |
8.154353 |
7.603566 |
| mch_2010_sd |
3.207965 |
2.971404 |
| mchc_2010_sd |
1.0994773 |
0.9629051 |
| rbc_2010_median |
4.650 |
4.735 |
| hgb_2010_median |
11.30 |
11.25 |
| mcv_2010_median |
75 |
74 |
| mch_2010_median |
24.95 |
24.50 |
| mchc_2010_median |
33.25 |
33.05 |
| rbc_2010_gmean |
4.611469 |
4.662481 |
| hgb_2010_gmean |
11.19391 |
11.15216 |
| mcv_2010_gmean |
73.21065 |
72.61669 |
| mch_2010_gmean |
24.27368 |
23.90224 |
| mchc_2010_gmean |
33.15629 |
32.91930 |
| rbc_2010_lci |
4.558738 |
4.539477 |
| hgb_2010_lci |
11.07644 |
10.88728 |
| mcv_2010_lci |
72.39146 |
70.89293 |
| mch_2010_lci |
23.94948 |
23.22589 |
| mchc_2010_lci |
33.04744 |
32.70131 |
| rbc_2010_uci |
4.664810 |
4.788817 |
| hgb_2010_uci |
11.31264 |
11.42348 |
| mcv_2010_uci |
74.03911 |
74.38237 |
| mch_2010_uci |
24.60227 |
24.59828 |
| mchc_2010_uci |
33.26550 |
33.13873 |
| rbc_2010_Gsd |
1.123788 |
1.125898 |
| hgb_2010_Gsd |
1.113003 |
1.112504 |
| mcv_2010_Gsd |
1.120963 |
1.112438 |
| mch_2010_Gsd |
1.146193 |
1.135771 |
| mchc_2010_Gsd |
1.033931 |
1.029905 |
| rbc_2010_min |
3.03 |
2.92 |
| hgb_2010_min |
8.2 |
8.2 |
| mcv_2010_min |
50 |
53 |
| mch_2010_min |
15.3 |
16.1 |
| mchc_2010_min |
29.2 |
30.4 |
| rbc_2010_max |
6.14 |
5.65 |
| hgb_2010_max |
14.1 |
14.0 |
| mcv_2010_max |
97 |
92 |
| mch_2010_max |
35 |
32 |
| mchc_2010_max |
36.6 |
35.0 |
| rbc_2010_IQR |
0.7349998 |
0.7975000 |
| hgb_2010_IQR |
1.475 |
1.500 |
| mcv_2010_IQR |
11.00 |
10.75 |
| mch_2010_IQR |
4.775 |
4.425 |
| mchc_2010_IQR |
1.400002 |
1.275000 |
| rbc_2010_q25 |
4.2625 |
4.3075 |
| hgb_2010_q25 |
10.525 |
10.500 |
| mcv_2010_q25 |
68.00 |
67.25 |
| mch_2010_q25 |
22.000 |
21.825 |
| mchc_2010_q25 |
32.5 |
32.3 |
| rbc_2010_q75 |
4.9975 |
5.1050 |
| hgb_2010_q75 |
12 |
12 |
| mcv_2010_q75 |
79 |
78 |
| mch_2010_q75 |
26.775 |
26.250 |
| mchc_2010_q75 |
33.900 |
33.575 |
| g6pd_202_rtpcr |
NORM |
NORM |
NORM |
HET |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
HOM/HEMI |
| thal |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
| rbc_2010_mean |
4.446757 |
4.723217 |
5.149740 |
4.372174 |
4.440500 |
5.069167 |
4.297273 |
4.353158 |
4.937000 |
| hgb_2010_mean |
11.46036 |
11.25944 |
10.85325 |
11.56957 |
11.01500 |
11.06667 |
11.58636 |
11.48684 |
10.62000 |
| mcv_2010_mean |
77.14414 |
72.10909 |
64.95065 |
79.02174 |
75.16250 |
68.05833 |
81.46818 |
79.74474 |
68.00000 |
| mch_2010_mean |
25.99459 |
23.96783 |
21.11429 |
26.62174 |
24.83750 |
21.90833 |
27.22727 |
26.47368 |
21.51000 |
| mchc_2010_mean |
33.63063 |
33.20000 |
32.48961 |
33.65652 |
32.98500 |
32.14167 |
33.40455 |
33.16053 |
31.58000 |
| rbc_2010_se |
0.04852200 |
0.04149057 |
0.04596277 |
0.08794976 |
0.05448588 |
0.12746631 |
0.10432049 |
0.06725686 |
0.09234052 |
| hgb_2010_se |
0.1246537 |
0.1002176 |
0.1177016 |
0.1871816 |
0.1920720 |
0.3208260 |
0.2030392 |
0.1689298 |
0.2365493 |
| mcv_2010_se |
0.7773953 |
0.4835641 |
0.5784866 |
1.4044812 |
0.9495524 |
1.2981606 |
1.6549988 |
0.8921157 |
0.7888106 |
| mch_2010_se |
0.3086544 |
0.1981014 |
0.2071576 |
0.5067121 |
0.4000270 |
0.5928180 |
0.6578590 |
0.3496937 |
0.3092822 |
| mchc_2010_se |
0.10126791 |
0.08773387 |
0.09587501 |
0.14755260 |
0.17080317 |
0.28774428 |
0.19434389 |
0.13300012 |
0.24212041 |
| rbc_2010_sd |
0.5112110 |
0.4961551 |
0.4033216 |
0.4217922 |
0.3445990 |
0.4415562 |
0.4893065 |
0.4145992 |
0.2920064 |
| hgb_2010_sd |
1.3133080 |
1.1984284 |
1.0328276 |
0.8976913 |
1.2147702 |
1.1113739 |
0.9523382 |
1.0413534 |
0.7480345 |
| mcv_2010_sd |
8.190368 |
5.782586 |
5.076199 |
6.735655 |
6.005497 |
4.496960 |
7.762633 |
5.499371 |
2.494438 |
| mch_2010_sd |
3.2518761 |
2.3689482 |
1.8178005 |
2.4301059 |
2.5299932 |
2.0535818 |
3.0856321 |
2.1556567 |
0.9780362 |
| mchc_2010_sd |
1.0669236 |
1.0491444 |
0.8412998 |
0.7076374 |
1.0802541 |
0.9967754 |
0.9115536 |
0.8198678 |
0.7656520 |
| rbc_2010_median |
4.460 |
4.710 |
5.170 |
4.310 |
4.465 |
5.170 |
4.195 |
4.350 |
4.845 |
| hgb_2010_median |
11.40 |
11.30 |
10.80 |
11.60 |
11.30 |
11.35 |
11.50 |
11.30 |
10.40 |
| mcv_2010_median |
79.00 |
73.00 |
65.00 |
81.00 |
76.65 |
68.85 |
83.50 |
80.00 |
67.50 |
| mch_2010_median |
26.60 |
24.60 |
21.10 |
27.20 |
25.05 |
21.80 |
28.10 |
26.60 |
21.30 |
| mchc_2010_median |
33.60 |
33.30 |
32.40 |
33.70 |
32.90 |
31.95 |
33.50 |
33.15 |
31.55 |
| rbc_2010_gmean |
4.416555 |
4.697094 |
5.133558 |
4.352605 |
4.427274 |
5.050786 |
4.272956 |
4.333848 |
4.929318 |
| hgb_2010_gmean |
11.38290 |
11.19479 |
10.80398 |
11.53594 |
10.94647 |
11.00984 |
11.54979 |
11.44091 |
10.59692 |
| mcv_2010_gmean |
76.67026 |
71.86497 |
64.75478 |
78.72398 |
74.91062 |
67.92553 |
81.06952 |
79.54821 |
67.95942 |
| mch_2010_gmean |
25.76794 |
23.84068 |
21.03725 |
26.50871 |
24.69952 |
21.82474 |
27.03226 |
26.38084 |
21.49013 |
| mchc_2010_gmean |
33.61369 |
33.18323 |
32.47892 |
33.64941 |
32.96753 |
32.12766 |
33.39241 |
33.15064 |
31.57168 |
| rbc_2010_lci |
4.318934 |
4.615370 |
5.040351 |
4.173513 |
4.317430 |
4.770030 |
4.075221 |
4.199581 |
4.726711 |
| hgb_2010_lci |
11.13221 |
10.99590 |
10.56977 |
11.15211 |
10.55211 |
10.27370 |
11.14205 |
11.10437 |
10.08583 |
| mcv_2010_lci |
75.03648 |
70.87418 |
63.61291 |
75.69256 |
72.91041 |
65.18011 |
77.39008 |
77.67683 |
66.21784 |
| mch_2010_lci |
25.11012 |
23.42631 |
20.62927 |
25.43084 |
23.84849 |
20.60813 |
25.54045 |
25.63780 |
20.80575 |
| mchc_2010_lci |
33.41211 |
33.00787 |
32.28931 |
33.34505 |
32.62064 |
31.50581 |
32.98404 |
32.88197 |
31.03047 |
| rbc_2010_uci |
4.516382 |
4.780265 |
5.228489 |
4.539382 |
4.539913 |
5.348066 |
4.480286 |
4.472408 |
5.140609 |
| hgb_2010_uci |
11.63924 |
11.39729 |
11.04337 |
11.93299 |
11.35557 |
11.79873 |
11.97244 |
11.78764 |
11.13390 |
| mcv_2010_uci |
78.33961 |
72.86961 |
65.91714 |
81.87682 |
76.96571 |
70.78659 |
84.92390 |
81.46468 |
69.74681 |
| mch_2010_uci |
26.44300 |
24.26238 |
21.45329 |
27.63227 |
25.58092 |
23.11319 |
28.61120 |
27.14541 |
22.19702 |
| mchc_2010_uci |
33.81649 |
33.35953 |
32.66963 |
33.95655 |
33.31811 |
32.76178 |
33.80583 |
33.42151 |
32.12233 |
| rbc_2010_Gsd |
1.126174 |
1.112019 |
1.084078 |
1.102040 |
1.081725 |
1.094188 |
1.112783 |
1.100480 |
1.060427 |
| hgb_2010_Gsd |
1.125685 |
1.114542 |
1.101373 |
1.081397 |
1.121568 |
1.115069 |
1.084437 |
1.095087 |
1.071544 |
| mcv_2010_Gsd |
1.121324 |
1.087608 |
1.081538 |
1.095058 |
1.088309 |
1.067089 |
1.110445 |
1.075115 |
1.036957 |
| mch_2010_Gsd |
1.147380 |
1.111895 |
1.090114 |
1.100753 |
1.115870 |
1.094477 |
1.136592 |
1.090810 |
1.046281 |
| mchc_2010_Gsd |
1.032494 |
1.032573 |
1.026131 |
1.021234 |
1.033628 |
1.031240 |
1.028141 |
1.025066 |
1.024466 |
| rbc_2010_min |
2.92 |
3.36 |
4.00 |
3.41 |
3.76 |
4.32 |
3.73 |
3.62 |
4.57 |
| hgb_2010_min |
8.2 |
8.5 |
8.5 |
9.7 |
8.2 |
8.4 |
10.0 |
9.2 |
9.8 |
| mcv_2010_min |
51.0 |
54.0 |
50.0 |
59.0 |
58.0 |
61.0 |
56.6 |
63.0 |
65.0 |
| mch_2010_min |
15.8 |
16.5 |
15.3 |
19.9 |
17.8 |
18.8 |
17.3 |
19.6 |
20.1 |
| mchc_2010_min |
30.4 |
29.2 |
30.6 |
32.3 |
30.4 |
30.4 |
30.7 |
31.2 |
30.6 |
| rbc_2010_max |
5.86 |
6.14 |
6.04 |
5.28 |
5.03 |
5.66 |
5.84 |
5.16 |
5.43 |
| hgb_2010_max |
14.1 |
13.8 |
13.3 |
13.4 |
13.2 |
12.1 |
14.1 |
14.1 |
12.1 |
| mcv_2010_max |
97.0 |
85.0 |
82.0 |
87.2 |
83.7 |
79.0 |
92.0 |
90.0 |
73.0 |
| mch_2010_max |
35.0 |
28.6 |
28.1 |
30.5 |
28.7 |
27.2 |
30.8 |
30.0 |
22.8 |
| mchc_2010_max |
36.6 |
35.3 |
34.3 |
35.0 |
34.9 |
34.4 |
34.5 |
35.2 |
32.9 |
| rbc_2010_IQR |
0.6099997 |
0.6650002 |
0.4400001 |
0.4800003 |
0.5400000 |
0.4425000 |
0.5825000 |
0.6674999 |
0.3799999 |
| hgb_2010_IQR |
1.5000000 |
1.5000000 |
1.1999998 |
1.2000003 |
1.4250002 |
0.7749999 |
1.1499999 |
1.4000001 |
0.9750001 |
| mcv_2010_IQR |
7.650002 |
6.000000 |
5.000000 |
8.500000 |
6.250000 |
3.000000 |
8.750000 |
3.950001 |
3.750000 |
| mch_2010_IQR |
3.250001 |
2.550000 |
1.500000 |
3.099999 |
2.450001 |
1.125000 |
3.074999 |
2.225000 |
1.474999 |
| mchc_2010_IQR |
1.1000004 |
1.2000008 |
1.1999989 |
0.8999996 |
1.1749983 |
1.0499997 |
1.1500006 |
1.2500000 |
1.0500002 |
| rbc_2010_q25 |
4.1300 |
4.3900 |
4.9500 |
4.1600 |
4.1900 |
4.9175 |
3.9425 |
3.9900 |
4.7675 |
| hgb_2010_q25 |
10.700 |
10.500 |
10.200 |
10.950 |
10.350 |
10.925 |
11.025 |
10.825 |
10.075 |
| mcv_2010_q25 |
74.50 |
70.00 |
62.00 |
75.50 |
72.75 |
66.00 |
78.00 |
78.00 |
66.00 |
| mch_2010_q25 |
24.850 |
22.950 |
20.300 |
25.250 |
24.175 |
21.325 |
26.150 |
25.500 |
21.000 |
| mchc_2010_q25 |
33.150 |
32.700 |
31.900 |
33.200 |
32.475 |
31.675 |
33.025 |
32.525 |
30.950 |
| rbc_2010_q75 |
4.7400 |
5.0550 |
5.3900 |
4.6400 |
4.7300 |
5.3600 |
4.5250 |
4.6575 |
5.1475 |
| hgb_2010_q75 |
12.200 |
12.000 |
11.400 |
12.150 |
11.775 |
11.700 |
12.175 |
12.225 |
11.050 |
| mcv_2010_q75 |
82.15 |
76.00 |
67.00 |
84.00 |
79.00 |
69.00 |
86.75 |
81.95 |
69.75 |
| mch_2010_q75 |
28.100 |
25.500 |
21.800 |
28.350 |
26.625 |
22.450 |
29.225 |
27.725 |
22.475 |
| mchc_2010_q75 |
34.250 |
33.900 |
33.100 |
34.100 |
33.650 |
32.725 |
34.175 |
33.775 |
32.000 |
| g6pd_202_rtpcr |
NORM |
NORM |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
| sickle |
NORM |
HET |
NORM |
HET |
NORM |
HET |
| rbc_2010_mean |
4.720394 |
4.779808 |
4.547705 |
4.400000 |
4.368103 |
4.665000 |
| hgb_2010_mean |
11.21326 |
11.33462 |
11.31475 |
10.66429 |
11.40690 |
11.33333 |
| mcv_2010_mean |
72.12079 |
72.19423 |
75.40000 |
74.37857 |
79.35517 |
75.00000 |
| mch_2010_mean |
23.99677 |
23.91346 |
25.03115 |
24.41429 |
26.31724 |
24.47500 |
| mchc_2010_mean |
33.20215 |
33.05577 |
33.12295 |
32.76429 |
33.09655 |
32.60000 |
| rbc_2010_se |
0.03280459 |
0.07500955 |
0.05677122 |
0.12881114 |
0.06030446 |
0.13568290 |
| hgb_2010_se |
0.07314583 |
0.16934985 |
0.13837140 |
0.32047479 |
0.13976175 |
0.22540647 |
| mcv_2010_se |
0.4787121 |
1.0760081 |
0.8425825 |
2.3025113 |
0.9988201 |
1.5275252 |
| mch_2010_se |
0.1903450 |
0.4270460 |
0.3541290 |
0.8718338 |
0.4030295 |
0.6336672 |
| mchc_2010_se |
0.06676123 |
0.13449008 |
0.13903243 |
0.27508388 |
0.13775848 |
0.23257463 |
| rbc_2010_sd |
0.5479447 |
0.5409016 |
0.4433974 |
0.4819672 |
0.4592651 |
0.4700193 |
| hgb_2010_sd |
1.2217762 |
1.2211991 |
1.0807152 |
1.1991069 |
1.0643938 |
0.7808309 |
| mcv_2010_sd |
7.996069 |
7.759205 |
6.580780 |
8.615208 |
7.606788 |
5.291503 |
| mch_2010_sd |
3.179389 |
3.079472 |
2.765836 |
3.262103 |
3.069381 |
2.195087 |
| mchc_2010_sd |
1.1151324 |
0.9698218 |
1.0858780 |
1.0292696 |
1.0491373 |
0.8056621 |
| rbc_2010_median |
4.730 |
4.860 |
4.510 |
4.325 |
4.320 |
4.660 |
| hgb_2010_median |
11.20 |
11.30 |
11.40 |
10.65 |
11.25 |
11.35 |
| mcv_2010_median |
73 |
72 |
77 |
76 |
81 |
77 |
| mch_2010_median |
24.60 |
24.05 |
25.30 |
24.85 |
26.70 |
25.05 |
| mchc_2010_median |
33.30 |
33.20 |
33.10 |
32.95 |
33.25 |
32.55 |
| rbc_2010_gmean |
4.687877 |
4.747404 |
4.526581 |
4.375946 |
4.345049 |
4.642839 |
| hgb_2010_gmean |
11.14546 |
11.26950 |
11.26081 |
10.59919 |
11.35858 |
11.30911 |
| mcv_2010_gmean |
71.66537 |
71.77997 |
75.10655 |
73.89061 |
78.97028 |
74.82308 |
| mch_2010_gmean |
23.77869 |
23.71387 |
24.87311 |
24.19966 |
26.12370 |
24.38164 |
| mchc_2010_gmean |
33.18335 |
33.04168 |
33.10527 |
32.74895 |
33.07997 |
32.59085 |
| rbc_2010_lci |
4.622645 |
4.590725 |
4.415159 |
4.110785 |
4.228732 |
4.349829 |
| hgb_2010_lci |
11.000376 |
10.933043 |
10.975293 |
9.911541 |
11.084654 |
10.830317 |
| mcv_2010_lci |
70.71009 |
69.63609 |
73.39492 |
68.91394 |
76.89192 |
71.45874 |
| mch_2010_lci |
23.39756 |
22.85803 |
24.15032 |
22.31813 |
25.27365 |
22.99518 |
| mchc_2010_lci |
33.05149 |
32.77091 |
32.82636 |
32.15062 |
32.80226 |
32.08205 |
| rbc_2010_uci |
4.754030 |
4.909430 |
4.640815 |
4.658212 |
4.464565 |
4.955587 |
| hgb_2010_uci |
11.29245 |
11.61631 |
11.55375 |
11.33454 |
11.63927 |
11.80907 |
| mcv_2010_uci |
72.63356 |
73.98985 |
76.85809 |
79.22667 |
81.10482 |
78.34581 |
| mch_2010_uci |
24.16604 |
24.60175 |
25.61755 |
26.23982 |
27.00235 |
25.85169 |
| mchc_2010_uci |
33.31574 |
33.31469 |
33.38655 |
33.35841 |
33.36002 |
33.10772 |
| rbc_2010_Gsd |
1.126258 |
1.128111 |
1.102205 |
1.114340 |
1.108712 |
1.108049 |
| hgb_2010_Gsd |
1.117591 |
1.115020 |
1.105476 |
1.123192 |
1.097288 |
1.070456 |
| mcv_2010_Gsd |
1.120601 |
1.115069 |
1.094187 |
1.128359 |
1.106757 |
1.075094 |
| mch_2010_Gsd |
1.146949 |
1.141144 |
1.122036 |
1.150484 |
1.134070 |
1.096523 |
| mchc_2010_Gsd |
1.034363 |
1.029998 |
1.033587 |
1.032451 |
1.032582 |
1.025074 |
| rbc_2010_min |
3.03 |
2.92 |
3.41 |
3.76 |
3.62 |
3.76 |
| hgb_2010_min |
8.2 |
8.4 |
8.3 |
8.2 |
9.2 |
10.1 |
| mcv_2010_min |
50.0 |
53.0 |
58.0 |
59.0 |
56.6 |
66.0 |
| mch_2010_min |
15.3 |
16.1 |
17.8 |
18.4 |
17.3 |
21.0 |
| mchc_2010_min |
29.2 |
30.4 |
30.4 |
30.4 |
30.6 |
31.1 |
| rbc_2010_max |
6.14 |
5.65 |
5.66 |
5.28 |
5.84 |
5.43 |
| hgb_2010_max |
14.1 |
14.0 |
13.4 |
12.3 |
14.1 |
13.1 |
| mcv_2010_max |
97.0 |
92.0 |
87.2 |
85.0 |
92.0 |
81.0 |
| mch_2010_max |
35.0 |
32.0 |
30.5 |
28.5 |
30.8 |
26.8 |
| mchc_2010_max |
36.6 |
35.0 |
35.0 |
34.3 |
35.2 |
33.9 |
| rbc_2010_IQR |
0.7249999 |
0.8125000 |
0.5900002 |
0.6300000 |
0.7000000 |
0.5825000 |
| hgb_2010_IQR |
1.5000000 |
1.5250001 |
1.1000004 |
1.4249997 |
1.5499997 |
0.5249996 |
| mcv_2010_IQR |
12.00 |
10.25 |
9.00 |
10.75 |
7.75 |
6.00 |
| mch_2010_IQR |
4.700001 |
4.000001 |
4.000000 |
4.075000 |
2.900000 |
3.224999 |
| mchc_2010_IQR |
1.400002 |
1.299999 |
1.299999 |
1.025002 |
1.549998 |
1.125002 |
| rbc_2010_q25 |
4.3750 |
4.3850 |
4.2200 |
4.1375 |
3.9825 |
4.3750 |
| hgb_2010_q25 |
10.500 |
10.500 |
10.900 |
10.225 |
10.625 |
11.025 |
| mcv_2010_q25 |
66.00 |
66.00 |
71.00 |
70.25 |
77.00 |
72.50 |
| mch_2010_q25 |
21.500 |
21.800 |
23.100 |
22.825 |
25.400 |
23.150 |
| mchc_2010_q25 |
32.500 |
32.400 |
32.500 |
32.375 |
32.400 |
32.075 |
| rbc_2010_q75 |
5.1000 |
5.1975 |
4.8100 |
4.7675 |
4.6825 |
4.9575 |
| hgb_2010_q75 |
12.000 |
12.025 |
12.000 |
11.650 |
12.175 |
11.550 |
| mcv_2010_q75 |
78.00 |
76.25 |
80.00 |
81.00 |
84.75 |
78.50 |
| mch_2010_q75 |
26.200 |
25.800 |
27.100 |
26.900 |
28.300 |
26.375 |
| mchc_2010_q75 |
33.90 |
33.70 |
33.80 |
33.40 |
33.95 |
33.20 |
CBC and enzyme activity summary in all individuals by sex
| sex |
FEMALE |
MALE |
| rbc_2010_mean |
4.648155 |
4.653745 |
| hgb_2010_mean |
11.21202 |
11.28642 |
| mcv_2010_mean |
73.27983 |
73.84733 |
| mch_2010_mean |
24.30558 |
24.54403 |
| mchc_2010_mean |
33.09785 |
33.17078 |
| rbc_2010_se |
0.03317732 |
0.03607628 |
| hgb_2010_se |
0.08100970 |
0.07178178 |
| mcv_2010_se |
0.5026479 |
0.5405404 |
| mch_2010_se |
0.2000205 |
0.2105351 |
| mchc_2010_se |
0.07047862 |
0.06973386 |
| rbc_2010_sd |
0.5064298 |
0.5623736 |
| hgb_2010_sd |
1.236559 |
1.118967 |
| mcv_2010_sd |
7.672588 |
8.426190 |
| mch_2010_sd |
3.053180 |
3.281918 |
| mchc_2010_sd |
1.075809 |
1.087043 |
| rbc_2010_median |
4.66 |
4.66 |
| hgb_2010_median |
11.2 |
11.3 |
| mcv_2010_median |
74 |
75 |
| mch_2010_median |
24.8 |
24.9 |
| mchc_2010_median |
33.2 |
33.2 |
| rbc_2010_gmean |
4.620184 |
4.619413 |
| hgb_2010_gmean |
11.14200 |
11.23044 |
| mcv_2010_gmean |
72.86195 |
73.35451 |
| mch_2010_gmean |
24.10360 |
24.31723 |
| mchc_2010_gmean |
33.08028 |
33.15292 |
| rbc_2010_lci |
4.554521 |
4.548330 |
| hgb_2010_lci |
10.98028 |
11.08878 |
| mcv_2010_lci |
71.84788 |
72.27773 |
| mch_2010_lci |
23.69740 |
23.89621 |
| mchc_2010_lci |
32.94075 |
33.01513 |
| rbc_2010_uci |
4.686793 |
4.691606 |
| hgb_2010_uci |
11.30611 |
11.37391 |
| mcv_2010_uci |
73.89033 |
74.44733 |
| mch_2010_uci |
24.51676 |
24.74568 |
| mchc_2010_uci |
33.22040 |
33.29128 |
| rbc_2010_Gsd |
1.117280 |
1.130569 |
| hgb_2010_Gsd |
1.119941 |
1.105679 |
| mcv_2010_Gsd |
1.114698 |
1.124149 |
| mch_2010_Gsd |
1.140735 |
1.148224 |
| mchc_2010_Gsd |
1.033290 |
1.033508 |
| rbc_2010_min |
2.92 |
3.03 |
| hgb_2010_min |
8.2 |
8.3 |
| mcv_2010_min |
50 |
54 |
| mch_2010_min |
15.3 |
16.5 |
| mchc_2010_min |
30.4 |
29.2 |
| rbc_2010_max |
5.94 |
6.14 |
| hgb_2010_max |
14.1 |
14.1 |
| mcv_2010_max |
91 |
97 |
| mch_2010_max |
30.6 |
35.0 |
| mchc_2010_max |
35.4 |
36.6 |
| rbc_2010_IQR |
0.6900001 |
0.8250003 |
| hgb_2010_IQR |
1.5 |
1.4 |
| mcv_2010_IQR |
10.0 |
12.5 |
| mch_2010_IQR |
4.500000 |
4.849999 |
| mchc_2010_IQR |
1.399998 |
1.450001 |
| rbc_2010_q25 |
4.310 |
4.225 |
| hgb_2010_q25 |
10.5 |
10.6 |
| mcv_2010_q25 |
68.0 |
67.5 |
| mch_2010_q25 |
21.90 |
21.95 |
| mchc_2010_q25 |
32.40 |
32.45 |
| rbc_2010_q75 |
5.00 |
5.05 |
| hgb_2010_q75 |
12 |
12 |
| mcv_2010_q75 |
78 |
80 |
| mch_2010_q75 |
26.4 |
26.8 |
| mchc_2010_q75 |
33.8 |
33.9 |
| sex |
FEMALE |
MALE |
| u_rcc_mean |
162.2613 |
150.2016 |
| u_ghb3_mean |
6.820365 |
6.304721 |
| u_rcc_se |
4.269510 |
5.343687 |
| u_ghb3_se |
0.1940967 |
0.2328067 |
| u_rcc_sd |
61.57575 |
75.75984 |
| u_ghb3_sd |
2.799303 |
3.300605 |
| u_rcc_median |
157.1490 |
150.2077 |
| u_ghb3_median |
6.602785 |
6.310665 |
| u_rcc_gmean |
148.1259 |
115.2248 |
| u_ghb3_gmean |
6.154935 |
4.763225 |
| u_rcc_lci |
138.7247 |
101.0028 |
| u_ghb3_lci |
5.743836 |
4.162161 |
| u_rcc_uci |
158.1641 |
131.4494 |
| u_ghb3_uci |
6.595457 |
5.451090 |
| u_rcc_Gsd |
1.615549 |
2.578361 |
| u_ghb3_Gsd |
1.658129 |
2.637496 |
| u_rcc_min |
9.524391 |
3.398979 |
| u_ghb3_min |
0.3349411 |
0.1435462 |
| u_rcc_max |
379.9057 |
395.3092 |
| u_ghb3_max |
16.16551 |
17.50067 |
| u_rcc_IQR |
79.70515 |
74.80269 |
| u_ghb3_IQR |
3.482317 |
3.692795 |
| u_rcc_q25 |
124.8328 |
117.5603 |
| u_ghb3_q25 |
4.91479 |
4.72858 |
| u_rcc_q75 |
204.538 |
192.363 |
| u_ghb3_q75 |
8.397107 |
8.421375 |
enzyme activity summary in all individuals by rbc polymorphisms
| g6pd_202_rtpcr |
NORM |
HET |
HOM/HEMI |
| u_rcc_mean |
175.73129 |
130.79786 |
51.81299 |
| u_ghb3_mean |
7.462490 |
5.269736 |
1.950350 |
| u_rcc_se |
3.241782 |
7.181025 |
10.785468 |
| u_ghb3_se |
0.1491342 |
0.2918838 |
0.3949996 |
| u_rcc_sd |
56.24281 |
59.65007 |
67.35523 |
| u_ghb3_sd |
2.587382 |
2.424569 |
2.466772 |
| u_rcc_median |
168.5269 |
124.7674 |
19.6063 |
| u_ghb3_median |
7.1370615 |
4.8094102 |
0.7131003 |
| u_rcc_gmean |
166.81705 |
116.04375 |
24.98386 |
| u_ghb3_gmean |
7.020193 |
4.660080 |
0.973574 |
| u_rcc_lci |
160.66704 |
101.83377 |
16.92674 |
| u_ghb3_lci |
6.7405903 |
4.0790528 |
0.6671128 |
| u_rcc_uci |
173.20247 |
132.23660 |
36.87617 |
| u_ghb3_uci |
7.311394 |
5.323869 |
1.420819 |
| u_rcc_Gsd |
1.392592 |
1.722472 |
3.323606 |
| u_ghb3_Gsd |
1.430920 |
1.740797 |
3.209538 |
| u_rcc_min |
54.192707 |
9.524391 |
3.398979 |
| u_ghb3_min |
2.1923413 |
0.3349411 |
0.1435462 |
| u_rcc_max |
395.3092 |
323.7014 |
230.9428 |
| u_ghb3_max |
17.500669 |
12.804825 |
8.183425 |
| u_rcc_IQR |
69.98789 |
71.91367 |
36.55003 |
| u_ghb3_IQR |
3.344205 |
3.318816 |
1.465082 |
| u_rcc_q25 |
139.64580 |
90.07294 |
10.59350 |
| u_ghb3_q25 |
5.6888450 |
3.5210330 |
0.3970277 |
| u_rcc_q75 |
209.63369 |
161.98661 |
47.14353 |
| u_ghb3_q75 |
9.033050 |
6.839849 |
1.862110 |
| thal |
NORM |
HET |
HOM |
| u_rcc_mean |
161.8223 |
152.9865 |
155.1423 |
| u_ghb3_mean |
6.270190 |
6.399309 |
7.397789 |
| u_rcc_se |
6.416529 |
4.882728 |
7.022266 |
| u_ghb3_se |
0.2594050 |
0.2133727 |
0.3575672 |
| u_rcc_sd |
74.27670 |
67.12642 |
65.12181 |
| u_ghb3_sd |
3.002830 |
2.933390 |
3.315941 |
| u_rcc_median |
153.3215 |
156.5205 |
155.8709 |
| u_ghb3_median |
6.042139 |
6.392890 |
7.321995 |
| u_rcc_gmean |
133.9029 |
129.1865 |
130.1844 |
| u_ghb3_gmean |
5.151669 |
5.324334 |
6.134898 |
| u_rcc_lci |
116.8078 |
116.5362 |
110.1858 |
| u_ghb3_lci |
4.489929 |
4.780178 |
5.172777 |
| u_rcc_uci |
153.4999 |
143.2101 |
153.8128 |
| u_ghb3_uci |
5.910939 |
5.930433 |
7.275972 |
| u_rcc_Gsd |
2.224099 |
2.050749 |
2.176914 |
| u_ghb3_Gsd |
2.235829 |
2.119836 |
2.215841 |
| u_rcc_min |
3.398979 |
7.005303 |
4.747196 |
| u_ghb3_min |
0.1435462 |
0.2845517 |
0.2212455 |
| u_rcc_max |
394.0039 |
395.3092 |
292.5809 |
| u_ghb3_max |
16.16551 |
17.50067 |
15.32939 |
| u_rcc_IQR |
79.86424 |
73.25184 |
83.76700 |
| u_ghb3_IQR |
2.945448 |
3.641410 |
4.395576 |
| u_rcc_q25 |
123.1510 |
118.4634 |
121.1082 |
| u_ghb3_q25 |
4.866270 |
4.809410 |
5.332668 |
| u_rcc_q75 |
203.0153 |
191.7152 |
204.8752 |
| u_ghb3_q75 |
7.811718 |
8.450820 |
9.728243 |
| sickle |
NORM |
HET |
| u_rcc_mean |
155.6922 |
160.1460 |
| u_ghb3_mean |
6.530363 |
6.784029 |
| u_rcc_se |
3.641282 |
9.776166 |
| u_ghb3_se |
0.1613550 |
0.4340063 |
| u_rcc_sd |
68.12215 |
75.09216 |
| u_ghb3_sd |
3.018675 |
3.333666 |
| u_rcc_median |
155.1213 |
156.5034 |
| u_ghb3_median |
6.397652 |
7.042066 |
| u_rcc_gmean |
131.5413 |
127.3266 |
| u_ghb3_gmean |
5.443626 |
5.325591 |
| u_rcc_lci |
121.8160 |
100.6649 |
| u_ghb3_lci |
5.028729 |
4.188419 |
| u_rcc_uci |
142.0430 |
161.0498 |
| u_ghb3_uci |
5.892754 |
6.771509 |
| u_rcc_Gsd |
2.076361 |
2.463542 |
| u_ghb3_Gsd |
2.125703 |
2.513598 |
| u_rcc_min |
4.747196 |
3.398979 |
| u_ghb3_min |
0.1993038 |
0.1435462 |
| u_rcc_max |
395.3092 |
379.9057 |
| u_ghb3_max |
17.50067 |
16.16551 |
| u_rcc_IQR |
75.21142 |
81.03408 |
| u_ghb3_IQR |
3.523785 |
3.960841 |
| u_rcc_q25 |
120.0910 |
130.1509 |
| u_ghb3_q25 |
4.850011 |
5.078157 |
| u_rcc_q75 |
195.3024 |
211.1850 |
| u_ghb3_q75 |
8.373796 |
9.038998 |
| g6pd_202_rtpcr |
NORM |
NORM |
NORM |
HET |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
HOM/HEMI |
| thal |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
| u_rcc_mean |
179.89556 |
173.55839 |
173.85298 |
131.07626 |
138.08170 |
103.93570 |
61.80690 |
49.22864 |
43.86131 |
| u_ghb3_mean |
7.021658 |
7.344688 |
8.326735 |
4.949580 |
5.625747 |
4.724458 |
2.199936 |
1.783038 |
2.060080 |
| u_rcc_se |
6.259244 |
4.664460 |
6.233015 |
15.321296 |
8.767284 |
13.822562 |
23.871089 |
14.150910 |
23.869008 |
| u_ghb3_se |
0.2592179 |
0.2093933 |
0.3303890 |
0.5901362 |
0.3720397 |
0.6601840 |
0.8474558 |
0.4832788 |
1.1209772 |
| u_rcc_sd |
62.59244 |
53.59057 |
51.77531 |
73.47836 |
52.60370 |
43.71078 |
79.17144 |
64.84762 |
63.15146 |
| u_ghb3_sd |
2.592179 |
2.405746 |
2.744417 |
2.830194 |
2.232238 |
2.087685 |
2.810693 |
2.214662 |
2.965827 |
| u_rcc_median |
171.40792 |
166.55579 |
168.52694 |
140.72343 |
126.61452 |
96.27991 |
18.17770 |
25.61314 |
12.15665 |
| u_ghb3_median |
6.5660543 |
7.1328089 |
7.9634582 |
4.9312332 |
4.9086140 |
4.3398043 |
0.6291067 |
0.9587304 |
0.5314767 |
| u_rcc_gmean |
170.07431 |
165.11716 |
165.41643 |
106.20012 |
129.29057 |
96.41881 |
24.72818 |
27.58785 |
18.85839 |
| u_ghb3_gmean |
6.5931288 |
6.9410467 |
7.8572884 |
4.0070161 |
5.2282248 |
4.3586336 |
0.9248896 |
1.0374472 |
0.8721432 |
| u_rcc_lci |
159.080600 |
156.048128 |
152.719332 |
76.099606 |
114.330120 |
72.210766 |
9.058405 |
17.450938 |
5.252061 |
| u_ghb3_lci |
6.1425579 |
6.5348537 |
7.2152265 |
2.8696796 |
4.5898998 |
3.2376510 |
0.3542192 |
0.6625504 |
0.2402892 |
| u_rcc_uci |
181.82777 |
174.71326 |
179.16918 |
148.20662 |
146.20865 |
128.74240 |
67.50449 |
43.61309 |
67.71413 |
| u_ghb3_uci |
7.076750 |
7.372488 |
8.556485 |
5.595112 |
5.955323 |
5.867738 |
2.414948 |
1.624475 |
3.165492 |
| u_rcc_Gsd |
1.400431 |
1.388307 |
1.394387 |
2.161311 |
1.438277 |
1.498030 |
4.458645 |
2.734979 |
3.983722 |
| u_ghb3_Gsd |
1.428677 |
1.419384 |
1.425989 |
2.164133 |
1.469390 |
1.515297 |
4.172927 |
2.678136 |
4.030398 |
| u_rcc_min |
59.345262 |
58.223396 |
54.192707 |
9.524391 |
71.269935 |
60.101576 |
3.398979 |
7.005303 |
4.747196 |
| u_ghb3_min |
2.1923413 |
2.2775992 |
2.8938906 |
0.3349411 |
2.8566873 |
2.6573836 |
0.1435462 |
0.2845517 |
0.2212455 |
| u_rcc_max |
394.0039 |
395.3092 |
292.5809 |
323.7014 |
267.9529 |
195.3719 |
228.3035 |
230.9428 |
175.6983 |
| u_ghb3_max |
16.165506 |
17.500669 |
15.329394 |
12.804825 |
10.048232 |
9.000467 |
8.038586 |
7.669843 |
8.183425 |
| u_rcc_IQR |
75.11862 |
60.38923 |
69.53059 |
86.29817 |
69.43030 |
65.77771 |
93.43933 |
19.61359 |
43.75616 |
| u_ghb3_IQR |
2.6703436 |
3.1560542 |
3.4924922 |
3.3751712 |
3.2837715 |
2.6903808 |
3.1075602 |
0.7780939 |
2.1083933 |
| u_rcc_q25 |
137.660144 |
144.972445 |
140.106062 |
77.406208 |
96.428559 |
66.099925 |
8.087665 |
13.232238 |
6.728672 |
| u_ghb3_q25 |
5.3188241 |
5.7187727 |
6.5557390 |
2.8038944 |
3.7541915 |
3.0470145 |
0.3370247 |
0.4946822 |
0.3169058 |
| u_rcc_q75 |
212.77876 |
205.36168 |
209.63665 |
163.70437 |
165.85886 |
131.87764 |
101.52700 |
32.84583 |
50.48483 |
| u_ghb3_q75 |
7.989168 |
8.874827 |
10.048231 |
6.179066 |
7.037963 |
5.737395 |
3.444585 |
1.272776 |
2.425299 |
| g6pd_202_rtpcr |
NORM |
NORM |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
| sickle |
NORM |
HET |
NORM |
HET |
NORM |
HET |
| u_rcc_mean |
173.24147 |
191.08522 |
135.20787 |
107.54505 |
53.95964 |
40.00639 |
| u_ghb3_mean |
7.358102 |
8.106213 |
5.421841 |
4.467729 |
1.982178 |
1.775297 |
| u_rcc_se |
3.472216 |
8.750118 |
8.146006 |
11.870397 |
11.876068 |
27.366569 |
| u_ghb3_se |
0.1598909 |
0.4031148 |
0.3296779 |
0.5393792 |
0.4142737 |
1.2881221 |
| u_rcc_sd |
55.88003 |
56.70724 |
62.03813 |
39.36965 |
68.22282 |
67.03413 |
| u_ghb3_sd |
2.573200 |
2.612483 |
2.510752 |
1.788919 |
2.379821 |
3.155242 |
| u_rcc_median |
166.03213 |
193.76543 |
126.69238 |
114.57294 |
25.61314 |
12.64523 |
| u_ghb3_median |
7.0337615 |
7.8311999 |
4.8374349 |
4.2557218 |
0.9587304 |
0.4842228 |
| u_rcc_gmean |
164.38458 |
182.63326 |
119.73766 |
98.37053 |
26.98372 |
16.35786 |
| u_ghb3_gmean |
6.9169097 |
7.6920766 |
4.7789884 |
4.0803357 |
1.0443686 |
0.6617628 |
| u_rcc_lci |
157.835885 |
165.650045 |
103.565769 |
70.825920 |
17.781513 |
3.918449 |
| u_ghb3_lci |
6.6186889 |
6.9265868 |
4.1161301 |
2.9598550 |
0.6997371 |
0.1522645 |
| u_rcc_uci |
171.20497 |
201.35768 |
138.43481 |
136.62740 |
40.94822 |
68.28712 |
| u_ghb3_uci |
7.228568 |
8.542164 |
5.548593 |
5.624985 |
1.558737 |
2.876114 |
| u_rcc_Gsd |
1.394086 |
1.367807 |
1.736428 |
1.630689 |
3.242142 |
3.902808 |
| u_ghb3_Gsd |
1.433588 |
1.399872 |
1.764516 |
1.612640 |
3.093759 |
4.055503 |
| u_rcc_min |
54.192707 |
80.937702 |
9.524391 |
38.647048 |
4.747196 |
3.398979 |
| u_ghb3_min |
2.1923413 |
3.1026119 |
0.3349411 |
1.5608904 |
0.1993038 |
0.1435462 |
| u_rcc_max |
395.3092 |
379.9057 |
323.7014 |
148.0792 |
230.9428 |
175.6983 |
| u_ghb3_max |
17.500669 |
16.165506 |
12.804825 |
7.514330 |
8.038586 |
8.183425 |
| u_rcc_IQR |
64.71086 |
70.08447 |
84.94576 |
49.99878 |
48.47420 |
16.32114 |
| u_ghb3_IQR |
3.1501264 |
3.0379678 |
3.3887858 |
1.8306944 |
1.5747720 |
0.5937863 |
| u_rcc_q25 |
139.212907 |
153.759688 |
90.954696 |
89.633317 |
10.633050 |
8.369708 |
| u_ghb3_q25 |
5.5784104 |
6.5915094 |
3.5283685 |
3.6772252 |
0.4019293 |
0.3327987 |
| u_rcc_q75 |
203.92377 |
223.84416 |
175.90046 |
139.63210 |
59.10725 |
24.69085 |
| u_ghb3_q75 |
8.728537 |
9.629477 |
6.917154 |
5.507920 |
1.976701 |
0.926585 |
Age summary in all individuals by rbc polymorphisms
| g6pd_202_rtpcr |
NORM |
NORM |
NORM |
HET |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
HOM/HEMI |
| thal |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
| mean |
7.325177 |
7.595225 |
7.788690 |
6.716342 |
7.151369 |
8.955353 |
7.527091 |
8.532616 |
7.447758 |
| se |
0.3190084 |
0.2752831 |
0.3630068 |
0.5989634 |
0.5713303 |
0.7347784 |
0.7448205 |
0.5293716 |
0.6509345 |
| sd |
3.360963 |
3.291907 |
3.185371 |
2.872528 |
3.613410 |
2.545347 |
3.493518 |
3.263265 |
2.058436 |
| median |
7.588809 |
7.635352 |
8.752738 |
7.073922 |
6.257615 |
9.302019 |
7.226129 |
9.060233 |
7.508984 |
| gmean |
6.229636 |
6.656560 |
6.848250 |
5.910239 |
6.107502 |
8.558408 |
6.409244 |
7.674356 |
7.165436 |
| lci |
5.501694 |
6.046777 |
5.985891 |
4.589391 |
5.007157 |
6.927208 |
4.775661 |
6.456101 |
5.767511 |
| uci |
7.053894 |
7.327836 |
7.834844 |
7.611232 |
7.449652 |
10.573718 |
8.601617 |
9.122493 |
8.902189 |
| Gsd |
1.935979 |
1.788179 |
1.809356 |
1.794849 |
1.861044 |
1.394882 |
1.941705 |
1.691979 |
1.354435 |
| min |
0.7268994 |
0.7965435 |
0.7268994 |
1.0191650 |
1.0520192 |
4.0547570 |
1.5054757 |
1.3184463 |
4.0136893 |
| max |
13.48785 |
13.71321 |
13.53833 |
11.52738 |
14.01369 |
12.38809 |
13.64904 |
13.09155 |
10.64083 |
| IQR |
5.291667 |
4.940537 |
3.922142 |
4.238450 |
5.303217 |
2.699435 |
4.667736 |
4.293036 |
1.624914 |
| q25 |
4.645534 |
5.349162 |
5.554757 |
4.681725 |
4.650026 |
7.802019 |
5.947767 |
6.892112 |
6.754877 |
| q75 |
9.937201 |
10.289699 |
9.476899 |
8.920175 |
9.953243 |
10.501454 |
10.615503 |
11.185147 |
8.379791 |
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |
| g6pd_202_rtpcr |
NORM |
NORM |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
| sickle |
NORM |
HET |
NORM |
HET |
NORM |
HET |
| mean |
7.486841 |
7.886777 |
7.558533 |
6.208883 |
8.090857 |
7.920274 |
| se |
0.1979140 |
0.4418243 |
0.4232514 |
0.8360296 |
0.4248043 |
0.9130371 |
| sd |
3.305815 |
3.186040 |
3.305699 |
3.128136 |
3.235213 |
3.162853 |
| median |
7.774641 |
8.462440 |
7.821184 |
5.403747 |
8.177019 |
7.963809 |
| gmean |
6.495556 |
6.872437 |
6.654887 |
5.316352 |
7.172940 |
7.221224 |
| lci |
6.046919 |
5.760380 |
5.759202 |
3.670686 |
6.181868 |
5.324201 |
| uci |
6.977478 |
8.199179 |
7.689871 |
7.699815 |
8.322900 |
9.794159 |
| Gsd |
1.835424 |
1.885194 |
1.758413 |
1.899386 |
1.760358 |
1.615526 |
| min |
0.7268994 |
0.7883299 |
1.0520192 |
1.0191650 |
1.3184463 |
2.8129706 |
| max |
13.71321 |
13.48785 |
14.01369 |
10.87988 |
13.64904 |
12.75548 |
| IQR |
4.922142 |
4.302319 |
4.590007 |
5.147202 |
4.542351 |
4.536875 |
| q25 |
5.042437 |
6.082349 |
5.246064 |
3.837440 |
6.370979 |
6.133299 |
| q75 |
9.964579 |
10.384668 |
9.836071 |
8.984642 |
10.913330 |
10.670175 |
| g6pd_202_rtpcr |
NORM |
HET |
HOM/HEMI |
| mean |
7.549671 |
7.306598 |
8.061614 |
| se |
0.1806030 |
0.3805184 |
0.3825721 |
| sd |
3.285783 |
3.295386 |
3.200828 |
| median |
7.810233 |
7.073922 |
8.177019 |
| gmean |
6.553366 |
6.381690 |
7.181195 |
| lci |
6.134463 |
5.582296 |
6.300793 |
| uci |
7.000873 |
7.295558 |
8.184614 |
| Gsd |
1.842122 |
1.789044 |
1.730692 |
| min |
0.7268994 |
1.0191650 |
1.3184463 |
| max |
13.71321 |
14.01369 |
13.64904 |
| IQR |
4.676848 |
4.918036 |
4.409822 |
| q25 |
5.349162 |
4.814938 |
6.364819 |
| q75 |
10.026010 |
9.732974 |
10.774641 |
| thal |
NORM |
HET |
HOM |
| mean |
7.263888 |
7.676070 |
7.895666 |
| se |
0.2643119 |
0.2259335 |
0.3040482 |
| sd |
3.301254 |
3.358742 |
3.025242 |
| median |
7.389459 |
7.810233 |
8.659993 |
| gmean |
6.206310 |
6.715958 |
7.068068 |
| lci |
5.602761 |
6.217991 |
6.337948 |
| uci |
6.874876 |
7.253805 |
7.882296 |
| Gsd |
1.909551 |
1.788042 |
1.727497 |
| min |
0.7268994 |
0.7965435 |
0.7268994 |
| max |
13.64904 |
14.01369 |
13.53833 |
| IQR |
5.154646 |
5.303217 |
4.271133 |
| q25 |
4.718686 |
5.229637 |
5.667437 |
| q75 |
9.873332 |
10.532854 |
9.938569 |
| sickle |
NORM |
HET |
| mean |
7.585851 |
7.590770 |
| se |
0.1651191 |
0.3621115 |
| sd |
3.294116 |
3.198083 |
| median |
7.831366 |
7.653747 |
| gmean |
6.614659 |
6.613117 |
| lci |
6.238238 |
5.755551 |
| uci |
7.013795 |
7.598457 |
| Gsd |
1.812232 |
1.851537 |
| min |
0.7268994 |
0.7883299 |
| max |
14.01369 |
13.48785 |
| IQR |
4.805955 |
4.742556 |
| q25 |
5.233744 |
5.491188 |
| q75 |
10.03970 |
10.23374 |
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |
Age summary in individuals with g6pd activity by rbc polymorphisms
| g6pd_202_rtpcr |
NORM |
NORM |
NORM |
HET |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
HOM/HEMI |
| thal |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
NORM |
HET |
HOM |
| mean |
7.408984 |
7.591440 |
7.847372 |
6.716342 |
7.307219 |
9.311294 |
6.557338 |
8.469775 |
7.407362 |
| se |
0.3409776 |
0.2850034 |
0.3941853 |
0.5989634 |
0.6090908 |
0.7006753 |
1.1183103 |
0.7690894 |
0.8036949 |
| sd |
3.409776 |
3.286820 |
3.274349 |
2.872528 |
3.654545 |
2.215730 |
3.873941 |
3.607349 |
2.273192 |
| median |
7.752139 |
7.718686 |
8.774641 |
7.073922 |
6.461841 |
9.302019 |
6.883984 |
9.060233 |
7.508984 |
| min |
0.7268994 |
0.7965435 |
0.7268994 |
1.0191650 |
1.0520192 |
5.4276181 |
1.5054757 |
1.3184463 |
4.0136893 |
| max |
13.48785 |
13.71321 |
13.53833 |
11.52738 |
14.01369 |
12.38809 |
11.25274 |
13.09155 |
10.64083 |
| IQR |
5.407084 |
4.922142 |
4.308693 |
4.238450 |
5.123631 |
2.549795 |
7.752353 |
4.819644 |
1.812500 |
| q25 |
4.557110 |
5.307495 |
5.618925 |
4.681725 |
4.829612 |
8.274726 |
2.533924 |
6.416367 |
6.512705 |
| q75 |
9.964194 |
10.229637 |
9.927618 |
8.920175 |
9.953243 |
10.824521 |
10.286277 |
11.236011 |
8.325205 |
| NORM |
NORM |
100 |
| NORM |
HET |
133 |
| NORM |
HOM |
69 |
| HET |
NORM |
23 |
| HET |
HET |
36 |
| HET |
HOM |
10 |
| HOM/HEMI |
NORM |
12 |
| HOM/HEMI |
HET |
22 |
| HOM/HEMI |
HOM |
8 |
| g6pd_202_rtpcr |
NORM |
NORM |
HET |
HET |
HOM/HEMI |
HOM/HEMI |
| sickle |
NORM |
HET |
NORM |
HET |
NORM |
HET |
| mean |
7.508235 |
8.092561 |
7.633582 |
6.172811 |
7.694901 |
7.877595 |
| se |
0.2060905 |
0.5063202 |
0.4339953 |
0.9454864 |
0.5862927 |
1.5342056 |
| sd |
3.323110 |
3.281330 |
3.305210 |
3.135823 |
3.517756 |
3.758021 |
| median |
7.806126 |
8.865589 |
7.851899 |
5.454997 |
7.651010 |
8.214579 |
| min |
0.7268994 |
0.7883299 |
1.0520192 |
1.0191650 |
1.3184463 |
2.8129706 |
| max |
13.71321 |
13.48785 |
14.01369 |
10.87988 |
13.09155 |
12.75548 |
| IQR |
4.986781 |
4.570029 |
4.350060 |
4.372262 |
5.542437 |
5.095654 |
| q25 |
5.005176 |
6.124701 |
5.434463 |
4.091547 |
5.560832 |
5.231306 |
| q75 |
9.991958 |
10.694730 |
9.784523 |
8.463809 |
11.103268 |
10.326959 |
| NORM |
NORM |
260 |
| NORM |
HET |
42 |
| HET |
NORM |
58 |
| HET |
HET |
11 |
| HOM/HEMI |
NORM |
36 |
| HOM/HEMI |
HET |
6 |
CBC summary in malarianegative individuals
| g6pd_202_rtpcr |
NORM |
HET |
HOM/HEMI |
| rbc_2010_mean |
4.740000 |
4.533333 |
4.414127 |
| hgb_2010_mean |
11.21612 |
11.24783 |
11.39206 |
| mcv_2010_mean |
71.95099 |
75.29565 |
78.71270 |
| mch_2010_mean |
23.89967 |
24.97101 |
26.03016 |
| mchc_2010_mean |
33.14046 |
33.08696 |
33.00317 |
| rbc_2010_se |
0.03127269 |
0.05397841 |
0.05973257 |
| hgb_2010_se |
0.0704562 |
0.1272137 |
0.1328775 |
| mcv_2010_se |
0.4576118 |
0.8392453 |
0.9579524 |
| mch_2010_se |
0.1825739 |
0.3409849 |
0.3883060 |
| mchc_2010_se |
0.0630792 |
0.1273528 |
0.1327168 |
| rbc_2010_sd |
0.5452580 |
0.4483783 |
0.4741125 |
| hgb_2010_sd |
1.228446 |
1.056716 |
1.054683 |
| mcv_2010_sd |
7.978734 |
6.971295 |
7.603511 |
| mch_2010_sd |
3.183285 |
2.832433 |
3.082083 |
| mchc_2010_sd |
1.099823 |
1.057871 |
1.053407 |
| rbc_2010_median |
4.74 |
4.47 |
4.36 |
| hgb_2010_median |
11.2 |
11.4 |
11.3 |
| mcv_2010_median |
73 |
77 |
80 |
| mch_2010_median |
24.5 |
25.3 |
26.6 |
| mchc_2010_median |
33.2 |
33.1 |
33.1 |
| rbc_2010_gmean |
4.707758 |
4.511708 |
4.389640 |
| hgb_2010_gmean |
11.14776 |
11.19600 |
11.34458 |
| mcv_2010_gmean |
71.49740 |
74.96320 |
78.32899 |
| mch_2010_gmean |
23.68047 |
24.80413 |
25.83579 |
| mchc_2010_gmean |
33.12212 |
33.07013 |
32.98644 |
| rbc_2010_lci |
4.645321 |
4.406293 |
4.274133 |
| hgb_2010_lci |
11.00832 |
10.93442 |
11.08481 |
| mcv_2010_lci |
70.58582 |
73.25515 |
76.35556 |
| mch_2010_lci |
23.31537 |
24.10836 |
25.02775 |
| mchc_2010_lci |
32.99747 |
32.81518 |
32.72001 |
| rbc_2010_uci |
4.771034 |
4.619645 |
4.508268 |
| hgb_2010_uci |
11.28896 |
11.46383 |
11.61044 |
| mcv_2010_uci |
72.42076 |
76.71108 |
80.35341 |
| mch_2010_uci |
24.05129 |
25.51998 |
26.66990 |
| mchc_2010_uci |
33.24724 |
33.32706 |
33.25504 |
| rbc_2010_Gsd |
1.125578 |
1.103422 |
1.111690 |
| hgb_2010_Gsd |
1.117982 |
1.103415 |
1.096341 |
| mcv_2010_Gsd |
1.12041 |
1.10070 |
1.10663 |
| mch_2010_Gsd |
1.147597 |
1.125735 |
1.134474 |
| mchc_2010_Gsd |
1.033970 |
1.032741 |
1.032725 |
| rbc_2010_min |
2.92 |
3.41 |
3.62 |
| hgb_2010_min |
8.2 |
8.2 |
9.2 |
| mcv_2010_min |
50.0 |
58.0 |
56.6 |
| mch_2010_min |
15.3 |
17.8 |
17.3 |
| mchc_2010_min |
29.2 |
30.4 |
30.6 |
| rbc_2010_max |
6.14 |
5.66 |
5.84 |
| hgb_2010_max |
14.0 |
13.4 |
14.1 |
| mcv_2010_max |
97.0 |
87.2 |
92.0 |
| mch_2010_max |
35.0 |
30.5 |
30.8 |
| mchc_2010_max |
36.0 |
35.0 |
35.2 |
| rbc_2010_IQR |
0.7399998 |
0.6199999 |
0.7600000 |
| hgb_2010_IQR |
1.6 |
1.3 |
1.5 |
| mcv_2010_IQR |
11.175 |
10.000 |
7.000 |
| mch_2010_IQR |
4.600000 |
4.000000 |
2.949999 |
| mchc_2010_IQR |
1.500000 |
1.299999 |
1.399998 |
| rbc_2010_q25 |
4.38 |
4.19 |
3.99 |
| hgb_2010_q25 |
10.4 |
10.7 |
10.6 |
| mcv_2010_q25 |
66 |
71 |
77 |
| mch_2010_q25 |
21.50 |
23.10 |
24.95 |
| mchc_2010_q25 |
32.4 |
32.5 |
32.4 |
| rbc_2010_q75 |
5.12 |
4.81 |
4.75 |
| hgb_2010_q75 |
12.0 |
12.0 |
12.1 |
| mcv_2010_q75 |
77.175 |
81.000 |
84.000 |
| mch_2010_q75 |
26.1 |
27.1 |
27.9 |
| mchc_2010_q75 |
33.9 |
33.8 |
33.8 |
| thal |
NORM |
HET |
HOM |
| rbc_2010_mean |
4.425035 |
4.615792 |
5.113226 |
| hgb_2010_mean |
11.46170 |
11.27426 |
10.86022 |
| mcv_2010_mean |
77.70993 |
74.02178 |
65.78387 |
| mch_2010_mean |
26.14043 |
24.56980 |
21.28495 |
| mchc_2010_mean |
33.58227 |
33.14257 |
32.33333 |
| rbc_2010_se |
0.04169091 |
0.03442839 |
0.04163701 |
| hgb_2010_se |
0.10096625 |
0.08429247 |
0.10452748 |
| mcv_2010_se |
0.7005139 |
0.4635889 |
0.5227510 |
| mch_2010_se |
0.2728457 |
0.1830179 |
0.1891206 |
| mchc_2010_se |
0.08401213 |
0.07311559 |
0.08935033 |
| rbc_2010_sd |
0.4950521 |
0.4893194 |
0.4015328 |
| hgb_2010_sd |
1.198908 |
1.198021 |
1.008026 |
| mcv_2010_sd |
8.318141 |
6.588836 |
5.041228 |
| mch_2010_sd |
3.239863 |
2.601172 |
1.823813 |
| mchc_2010_sd |
0.9975887 |
1.0391678 |
0.8616634 |
| rbc_2010_median |
4.400 |
4.625 |
5.150 |
| hgb_2010_median |
11.4 |
11.3 |
10.9 |
| mcv_2010_median |
79 |
75 |
66 |
| mch_2010_median |
26.7 |
24.9 |
21.3 |
| mchc_2010_median |
33.60 |
33.25 |
32.10 |
| rbc_2010_gmean |
4.397335 |
4.590040 |
5.097141 |
| hgb_2010_gmean |
11.39739 |
11.20926 |
10.81325 |
| mcv_2010_gmean |
77.22479 |
73.71449 |
65.59098 |
| mch_2010_gmean |
25.91656 |
24.42079 |
21.20825 |
| mchc_2010_gmean |
33.56735 |
33.12610 |
32.32205 |
| rbc_2010_lci |
4.315315 |
4.522905 |
5.013292 |
| hgb_2010_lci |
11.19507 |
11.04144 |
10.60570 |
| mcv_2010_lci |
75.75692 |
72.77158 |
64.55412 |
| mch_2010_lci |
25.33610 |
24.04027 |
20.83791 |
| mchc_2010_lci |
33.39998 |
32.98051 |
32.14601 |
| rbc_2010_uci |
4.480915 |
4.658173 |
5.182392 |
| hgb_2010_uci |
11.60337 |
11.37962 |
11.02485 |
| mcv_2010_uci |
78.72111 |
74.66961 |
66.64450 |
| mch_2010_uci |
26.51031 |
24.80734 |
21.58516 |
| mchc_2010_uci |
33.73556 |
33.27234 |
32.49906 |
| rbc_2010_Gsd |
1.119728 |
1.112048 |
1.083872 |
| hgb_2010_Gsd |
1.113575 |
1.114853 |
1.098671 |
| mcv_2010_Gsd |
1.122167 |
1.097234 |
1.080443 |
| mch_2010_Gsd |
1.145737 |
1.119852 |
1.089301 |
| mchc_2010_Gsd |
1.030477 |
1.032259 |
1.026873 |
| rbc_2010_min |
2.92 |
3.36 |
4.00 |
| hgb_2010_min |
8.2 |
8.2 |
8.5 |
| mcv_2010_min |
51 |
54 |
50 |
| mch_2010_min |
15.8 |
16.5 |
15.3 |
| mchc_2010_min |
30.4 |
29.2 |
30.6 |
| rbc_2010_max |
5.86 |
6.14 |
6.04 |
| hgb_2010_max |
14.1 |
14.1 |
13.3 |
| mcv_2010_max |
97 |
90 |
82 |
| mch_2010_max |
35.0 |
30.0 |
28.1 |
| mchc_2010_max |
36.0 |
35.3 |
34.4 |
| rbc_2010_IQR |
0.6199999 |
0.6449997 |
0.5400000 |
| hgb_2010_IQR |
1.500 |
1.575 |
1.200 |
| mcv_2010_IQR |
9.000000 |
7.000000 |
5.699997 |
| mch_2010_IQR |
3.200001 |
2.475000 |
1.500000 |
| mchc_2010_IQR |
1.100002 |
1.200001 |
1.200001 |
| rbc_2010_q25 |
4.1100 |
4.3025 |
4.8400 |
| hgb_2010_q25 |
10.7 |
10.5 |
10.2 |
| mcv_2010_q25 |
74 |
71 |
63 |
| mch_2010_q25 |
25.0 |
23.8 |
20.5 |
| mchc_2010_q25 |
33.1 |
32.6 |
31.7 |
| rbc_2010_q75 |
4.7300 |
4.9475 |
5.3800 |
| hgb_2010_q75 |
12.200 |
12.075 |
11.400 |
| mcv_2010_q75 |
83.0 |
78.0 |
68.7 |
| mch_2010_q75 |
28.200 |
26.275 |
22.000 |
| mchc_2010_q75 |
34.2 |
33.8 |
32.9 |
| sickle |
NORM |
HET |
| rbc_2010_mean |
4.648995 |
4.720882 |
| hgb_2010_mean |
11.24212 |
11.27059 |
| mcv_2010_mean |
73.55707 |
72.91765 |
| mch_2010_mean |
24.43043 |
24.08824 |
| mchc_2010_mean |
33.13886 |
32.96765 |
| rbc_2010_se |
0.02778447 |
0.06624864 |
| hgb_2010_se |
0.06135666 |
0.14461445 |
| mcv_2010_se |
0.4275096 |
0.9525921 |
| mch_2010_se |
0.1684181 |
0.3727819 |
| mchc_2010_se |
0.05732495 |
0.12122150 |
| rbc_2010_sd |
0.5329986 |
0.5463003 |
| hgb_2010_sd |
1.177025 |
1.192521 |
| mcv_2010_sd |
8.201057 |
7.855276 |
| mch_2010_sd |
3.230820 |
3.074038 |
| mchc_2010_sd |
1.0996832 |
0.9996181 |
| rbc_2010_median |
4.66 |
4.75 |
| hgb_2010_median |
11.3 |
11.3 |
| mcv_2010_median |
75 |
74 |
| mch_2010_median |
24.90 |
24.55 |
| mchc_2010_median |
33.20 |
33.15 |
| rbc_2010_gmean |
4.618354 |
4.687932 |
| hgb_2010_gmean |
11.17926 |
11.20729 |
| mcv_2010_gmean |
73.08430 |
72.48926 |
| mch_2010_gmean |
24.20721 |
23.88710 |
| mchc_2010_gmean |
33.12051 |
32.95252 |
| rbc_2010_lci |
4.563951 |
4.552249 |
| hgb_2010_lci |
11.05747 |
10.91899 |
| mcv_2010_lci |
72.22788 |
70.57925 |
| mch_2010_lci |
23.86797 |
23.13512 |
| mchc_2010_lci |
33.00728 |
32.70914 |
| rbc_2010_uci |
4.673407 |
4.827658 |
| hgb_2010_uci |
11.30239 |
11.50321 |
| mcv_2010_uci |
73.95088 |
74.45097 |
| mch_2010_uci |
24.55128 |
24.66352 |
| mchc_2010_uci |
33.23412 |
33.19771 |
| rbc_2010_Gsd |
1.122545 |
1.129006 |
| hgb_2010_Gsd |
1.112779 |
1.113678 |
| mcv_2010_Gsd |
1.121863 |
1.116632 |
| mch_2010_Gsd |
1.147607 |
1.141278 |
| mchc_2010_Gsd |
1.033972 |
1.031101 |
| rbc_2010_min |
3.03 |
2.92 |
| hgb_2010_min |
8.2 |
8.2 |
| mcv_2010_min |
50 |
53 |
| mch_2010_min |
15.3 |
16.1 |
| mchc_2010_min |
29.2 |
30.4 |
| rbc_2010_max |
6.14 |
5.65 |
| hgb_2010_max |
14.1 |
14.0 |
| mcv_2010_max |
97 |
92 |
| mch_2010_max |
35 |
32 |
| mchc_2010_max |
36 |
35 |
| rbc_2010_IQR |
0.74 |
0.83 |
| hgb_2010_IQR |
1.5 |
1.5 |
| mcv_2010_IQR |
11.00 |
11.25 |
| mch_2010_IQR |
4.800001 |
4.500000 |
| mchc_2010_IQR |
1.500000 |
1.324999 |
| rbc_2010_q25 |
4.2650 |
4.3225 |
| hgb_2010_q25 |
10.5 |
10.5 |
| mcv_2010_q25 |
68.00 |
66.75 |
| mch_2010_q25 |
21.9 |
21.8 |
| mchc_2010_q25 |
32.400 |
32.275 |
| rbc_2010_q75 |
5.0050 |
5.1525 |
| hgb_2010_q75 |
12 |
12 |
| mcv_2010_q75 |
79 |
78 |
| mch_2010_q75 |
26.7 |
26.3 |
| mchc_2010_q75 |
33.9 |
33.6 |
| g6pd_202_rtpcr |
NORM |
HET |
HOM/HEMI |
| mean |
90.67375 |
87.25373 |
98.45109 |
| se |
2.314287 |
4.895284 |
4.855127 |
| sd |
40.35097 |
40.66328 |
38.53637 |
| median |
94.22998 |
81.22998 |
98.65708 |
| min |
8.722793 |
12.229979 |
15.821355 |
| max |
164.5585 |
168.1643 |
163.7885 |
| IQR |
61.32392 |
60.86858 |
51.78645 |
| q25 |
61.13604 |
57.16427 |
79.77207 |
| q75 |
122.4600 |
118.0329 |
131.5585 |
| thal |
NORM |
HET |
HOM |
| mean |
86.46262 |
92.89741 |
94.95957 |
| se |
3.421193 |
2.907411 |
3.791681 |
| sd |
40.62442 |
41.32207 |
36.56565 |
| median |
88.85421 |
94.34497 |
103.91992 |
| min |
8.722793 |
9.558522 |
8.722793 |
| max |
163.7885 |
168.1643 |
162.4600 |
| q25 |
55.19713 |
61.47177 |
67.42710 |
| q75 |
118.2300 |
130.0703 |
119.3943 |
| sickle |
NORM |
HET |
| mean |
90.99433 |
92.67405 |
| se |
2.102832 |
4.799763 |
| sd |
40.33931 |
39.57986 |
| median |
94.26283 |
98.68070 |
| min |
8.722793 |
9.459959 |
| max |
168.1643 |
161.8542 |
| q25 |
60.68172 |
66.76283 |
| q75 |
122.4928 |
127.0703 |
| sickle |
NORM |
HET |
| mean |
6.404185 |
6.887079 |
| se |
0.1694122 |
0.4458780 |
| sd |
3.072859 |
3.246041 |
| median |
6.310665 |
7.081610 |
| min |
0.0000000 |
0.1435462 |
| max |
17.50067 |
16.16551 |
| q25 |
4.721074 |
5.359057 |
| q75 |
8.287201 |
9.207834 |
| sickle |
NORM |
HET |
| mean |
152.320 |
162.826 |
| se |
3.790715 |
10.166472 |
| sd |
68.75733 |
74.01303 |
| median |
152.3442 |
166.6988 |
| min |
0.000000 |
3.398979 |
| max |
395.3092 |
379.9057 |
| q25 |
117.7815 |
135.8634 |
| q75 |
192.3630 |
211.9437 |
##2011 age and CBC summary (mean, sd, median) by rbc polymorphisms
##Tabulating the number of individuals with the different genotype combinations including and excluding malariapositive individuals
## <B><U>g6pd_202_rtpcr by HbS genotypes in all individuals</U></B>
## $rbc_2010
## g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
## NORM 279 61 58 398
## HET 52 14 12 78
## Sum 331 75 70 476
##
## $hgb_2010
## g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
## NORM 279 61 58 398
## HET 52 14 12 78
## Sum 331 75 70 476
##
## $mcv_2010
## g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
## NORM 279 61 58 398
## HET 52 14 12 78
## Sum 331 75 70 476
##
## $mch_2010
## g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
## NORM 279 61 58 398
## HET 52 14 12 78
## Sum 331 75 70 476
##
## $mchc_2010
## g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
## NORM 279 61 58 398
## HET 52 14 12 78
## Sum 331 75 70 476
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals</U></B>
## $rbc_2010
## g6pd_202_rtpcr
## thal NORM HET HOM/HEMI Sum
## NORM 111 23 22 156
## HET 143 40 38 221
## HOM 77 12 10 99
## Sum 331 75 70 476
##
## $hgb_2010
## g6pd_202_rtpcr
## thal NORM HET HOM/HEMI Sum
## NORM 111 23 22 156
## HET 143 40 38 221
## HOM 77 12 10 99
## Sum 331 75 70 476
##
## $mcv_2010
## g6pd_202_rtpcr
## thal NORM HET HOM/HEMI Sum
## NORM 111 23 22 156
## HET 143 40 38 221
## HOM 77 12 10 99
## Sum 331 75 70 476
##
## $mch_2010
## g6pd_202_rtpcr
## thal NORM HET HOM/HEMI Sum
## NORM 111 23 22 156
## HET 143 40 38 221
## HOM 77 12 10 99
## Sum 331 75 70 476
##
## $mchc_2010
## g6pd_202_rtpcr
## thal NORM HET HOM/HEMI Sum
## NORM 111 23 22 156
## HET 143 40 38 221
## HOM 77 12 10 99
## Sum 331 75 70 476
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 111 23 22 156
##
## **HET** 143 40 38 221
##
## **HOM** 77 12 10 99
##
## **Sum** 331 75 70 476
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HBS genotypes in all individuals</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 279 61 58 398
##
## **HET** 52 14 12 78
##
## **Sum** 331 75 70 476
## ----------------------------------------
## <B><U>thal by HbS genotypes in all individuals</U></B>
##
## -----------------------------------
## NORM HET HOM Sum
## ---------- ------ ----- ----- -----
## **NORM** 130 186 82 398
##
## **HET** 26 35 17 78
##
## **Sum** 156 221 99 476
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in all individuals</U></B>
##
## --------------------------------------
## g6pd_202_rtpcr thal sickle n
## ---------------- ------ -------- -----
## NORM NORM NORM 94
##
## NORM NORM HET 17
##
## NORM HET NORM 122
##
## NORM HET HET 21
##
## NORM HOM NORM 63
##
## NORM HOM HET 14
##
## HET NORM NORM 17
##
## HET NORM HET 6
##
## HET HET NORM 32
##
## HET HET HET 8
##
## HET HOM NORM 12
##
## HOM/HEMI NORM NORM 19
##
## HOM/HEMI NORM HET 3
##
## HOM/HEMI HET NORM 32
##
## HOM/HEMI HET HET 6
##
## HOM/HEMI HOM NORM 7
##
## HOM/HEMI HOM HET 3
## --------------------------------------
##
## ----------------------------------------------
## g6pd_202_rtpcr thal sickle sex n
## ---------------- ------ -------- -------- ----
## NORM NORM NORM FEMALE 36
##
## NORM NORM NORM MALE 58
##
## NORM NORM HET FEMALE 8
##
## NORM NORM HET MALE 9
##
## NORM HET NORM FEMALE 57
##
## NORM HET NORM MALE 65
##
## NORM HET HET FEMALE 11
##
## NORM HET HET MALE 10
##
## NORM HOM NORM FEMALE 29
##
## NORM HOM NORM MALE 34
##
## NORM HOM HET FEMALE 5
##
## NORM HOM HET MALE 9
##
## HET NORM NORM FEMALE 17
##
## HET NORM HET FEMALE 6
##
## HET HET NORM FEMALE 32
##
## HET HET HET FEMALE 8
##
## HET HOM NORM FEMALE 12
##
## HOM/HEMI NORM NORM FEMALE 4
##
## HOM/HEMI NORM NORM MALE 15
##
## HOM/HEMI NORM HET FEMALE 1
##
## HOM/HEMI NORM HET MALE 2
##
## HOM/HEMI HET NORM FEMALE 4
##
## HOM/HEMI HET NORM MALE 28
##
## HOM/HEMI HET HET MALE 6
##
## HOM/HEMI HOM NORM FEMALE 2
##
## HOM/HEMI HOM NORM MALE 5
##
## HOM/HEMI HOM HET FEMALE 1
##
## HOM/HEMI HOM HET MALE 2
## ----------------------------------------------
##
## -------------------------------------------------------------
## g6pd_202_rtpcr thal sickle malaria_status n
## ---------------- ------ -------- ----------------------- ----
## NORM NORM NORM no_malaria 75
##
## NORM NORM NORM assymptomatic_malaria 9
##
## NORM NORM NORM uncomplicated_malaria 10
##
## NORM NORM HET no_malaria 13
##
## NORM NORM HET assymptomatic_malaria 3
##
## NORM NORM HET uncomplicated_malaria 1
##
## NORM HET NORM no_malaria 99
##
## NORM HET NORM assymptomatic_malaria 14
##
## NORM HET NORM uncomplicated_malaria 9
##
## NORM HET HET no_malaria 17
##
## NORM HET HET assymptomatic_malaria 1
##
## NORM HET HET uncomplicated_malaria 3
##
## NORM HOM NORM no_malaria 48
##
## NORM HOM NORM assymptomatic_malaria 11
##
## NORM HOM NORM uncomplicated_malaria 4
##
## NORM HOM HET no_malaria 12
##
## NORM HOM HET assymptomatic_malaria 2
##
## HET NORM NORM no_malaria 14
##
## HET NORM NORM assymptomatic_malaria 2
##
## HET NORM NORM uncomplicated_malaria 1
##
## HET NORM HET no_malaria 4
##
## HET NORM HET assymptomatic_malaria 1
##
## HET NORM HET uncomplicated_malaria 1
##
## HET HET NORM no_malaria 24
##
## HET HET NORM assymptomatic_malaria 7
##
## HET HET NORM uncomplicated_malaria 1
##
## HET HET HET no_malaria 5
##
## HET HET HET assymptomatic_malaria 1
##
## HET HET HET uncomplicated_malaria 2
##
## HET HOM NORM no_malaria 11
##
## HET HOM NORM uncomplicated_malaria 1
##
## HOM/HEMI NORM NORM no_malaria 16
##
## HOM/HEMI NORM NORM assymptomatic_malaria 1
##
## HOM/HEMI NORM NORM uncomplicated_malaria 2
##
## HOM/HEMI NORM HET no_malaria 3
##
## HOM/HEMI HET NORM no_malaria 25
##
## HOM/HEMI HET NORM assymptomatic_malaria 5
##
## HOM/HEMI HET NORM uncomplicated_malaria 2
##
## HOM/HEMI HET HET no_malaria 4
##
## HOM/HEMI HET HET uncomplicated_malaria 2
##
## HOM/HEMI HOM NORM no_malaria 6
##
## HOM/HEMI HOM NORM assymptomatic_malaria 1
##
## HOM/HEMI HOM HET no_malaria 2
##
## HOM/HEMI HOM HET uncomplicated_malaria 1
## -------------------------------------------------------------
##
## --------------------------------------
## g6pd_202_rtpcr thal sickle n
## ---------------- ------ -------- -----
## NORM NORM NORM 85
##
## NORM NORM HET 15
##
## NORM HET NORM 118
##
## NORM HET HET 15
##
## NORM HOM NORM 57
##
## NORM HOM HET 12
##
## HET NORM NORM 17
##
## HET NORM HET 6
##
## HET HET NORM 31
##
## HET HET HET 5
##
## HET HOM NORM 10
##
## HOM/HEMI NORM NORM 11
##
## HOM/HEMI NORM HET 1
##
## HOM/HEMI HET NORM 18
##
## HOM/HEMI HET HET 4
##
## HOM/HEMI HOM NORM 7
##
## HOM/HEMI HOM HET 1
## --------------------------------------
##
## -----------------------------
## g6pd_202_rtpcr thal n
## ---------------- ------ -----
## NORM NORM 111
##
## NORM HET 143
##
## NORM HOM 77
##
## HET NORM 23
##
## HET HET 40
##
## HET HOM 12
##
## HOM/HEMI NORM 22
##
## HOM/HEMI HET 38
##
## HOM/HEMI HOM 10
## -----------------------------
##
## -------------------------------------
## g6pd_202_rtpcr thal sex n
## ---------------- ------ -------- ----
## NORM NORM FEMALE 44
##
## NORM NORM MALE 67
##
## NORM HET FEMALE 68
##
## NORM HET MALE 75
##
## NORM HOM FEMALE 34
##
## NORM HOM MALE 43
##
## HET NORM FEMALE 23
##
## HET HET FEMALE 40
##
## HET HOM FEMALE 12
##
## HOM/HEMI NORM FEMALE 5
##
## HOM/HEMI NORM MALE 17
##
## HOM/HEMI HET FEMALE 4
##
## HOM/HEMI HET MALE 34
##
## HOM/HEMI HOM FEMALE 3
##
## HOM/HEMI HOM MALE 7
## -------------------------------------
##
## -----------------------------------------------------
## g6pd_202_rtpcr thal malaria_status n
## ---------------- ------ ----------------------- -----
## NORM NORM no_malaria 88
##
## NORM NORM assymptomatic_malaria 12
##
## NORM NORM uncomplicated_malaria 11
##
## NORM HET no_malaria 116
##
## NORM HET assymptomatic_malaria 15
##
## NORM HET uncomplicated_malaria 12
##
## NORM HOM no_malaria 60
##
## NORM HOM assymptomatic_malaria 13
##
## NORM HOM uncomplicated_malaria 4
##
## HET NORM no_malaria 18
##
## HET NORM assymptomatic_malaria 3
##
## HET NORM uncomplicated_malaria 2
##
## HET HET no_malaria 29
##
## HET HET assymptomatic_malaria 8
##
## HET HET uncomplicated_malaria 3
##
## HET HOM no_malaria 11
##
## HET HOM uncomplicated_malaria 1
##
## HOM/HEMI NORM no_malaria 19
##
## HOM/HEMI NORM assymptomatic_malaria 1
##
## HOM/HEMI NORM uncomplicated_malaria 2
##
## HOM/HEMI HET no_malaria 29
##
## HOM/HEMI HET assymptomatic_malaria 5
##
## HOM/HEMI HET uncomplicated_malaria 4
##
## HOM/HEMI HOM no_malaria 8
##
## HOM/HEMI HOM assymptomatic_malaria 1
##
## HOM/HEMI HOM uncomplicated_malaria 1
## -----------------------------------------------------
##
## -----------------------------
## g6pd_202_rtpcr thal n
## ---------------- ------ -----
## NORM NORM 100
##
## NORM HET 133
##
## NORM HOM 69
##
## HET NORM 23
##
## HET HET 36
##
## HET HOM 10
##
## HOM/HEMI NORM 12
##
## HOM/HEMI HET 22
##
## HOM/HEMI HOM 8
## -----------------------------
##
## -------------------------------
## g6pd_202_rtpcr sickle n
## ---------------- -------- -----
## NORM NORM 279
##
## NORM HET 52
##
## HET NORM 61
##
## HET HET 14
##
## HOM/HEMI NORM 58
##
## HOM/HEMI HET 12
## -------------------------------
##
## -------------------------------------------------------
## g6pd_202_rtpcr sickle malaria_status n
## ---------------- -------- ----------------------- -----
## NORM NORM no_malaria 222
##
## NORM NORM assymptomatic_malaria 34
##
## NORM NORM uncomplicated_malaria 23
##
## NORM HET no_malaria 42
##
## NORM HET assymptomatic_malaria 6
##
## NORM HET uncomplicated_malaria 4
##
## HET NORM no_malaria 49
##
## HET NORM assymptomatic_malaria 9
##
## HET NORM uncomplicated_malaria 3
##
## HET HET no_malaria 9
##
## HET HET assymptomatic_malaria 2
##
## HET HET uncomplicated_malaria 3
##
## HOM/HEMI NORM no_malaria 47
##
## HOM/HEMI NORM assymptomatic_malaria 7
##
## HOM/HEMI NORM uncomplicated_malaria 4
##
## HOM/HEMI HET no_malaria 9
##
## HOM/HEMI HET uncomplicated_malaria 3
## -------------------------------------------------------
##
## ----------------------------------------
## g6pd_202_rtpcr sickle sex n
## ---------------- -------- -------- -----
## NORM NORM FEMALE 122
##
## NORM NORM MALE 157
##
## NORM HET FEMALE 24
##
## NORM HET MALE 28
##
## HET NORM FEMALE 61
##
## HET HET FEMALE 14
##
## HOM/HEMI NORM FEMALE 10
##
## HOM/HEMI NORM MALE 48
##
## HOM/HEMI HET FEMALE 2
##
## HOM/HEMI HET MALE 10
## ----------------------------------------
##
## -------------------------------
## g6pd_202_rtpcr sickle n
## ---------------- -------- -----
## NORM NORM 260
##
## NORM HET 42
##
## HET NORM 58
##
## HET HET 11
##
## HOM/HEMI NORM 36
##
## HOM/HEMI HET 6
## -------------------------------
## <B><U>g6pd_202_rtpcr, thal and HbS genotypes by sex and malaria status</U></B>
##
## ----------------------------------------------
## g6pd_202_rtpcr malaria_status n
## ---------------- ----------------------- -----
## NORM no_malaria 264
##
## NORM assymptomatic_malaria 40
##
## NORM uncomplicated_malaria 27
##
## HET no_malaria 58
##
## HET assymptomatic_malaria 11
##
## HET uncomplicated_malaria 6
##
## HOM/HEMI no_malaria 56
##
## HOM/HEMI assymptomatic_malaria 7
##
## HOM/HEMI uncomplicated_malaria 7
## ----------------------------------------------
##
## -------------------------------
## g6pd_202_rtpcr sex n
## ---------------- -------- -----
## NORM FEMALE 146
##
## NORM MALE 185
##
## HET FEMALE 75
##
## HOM/HEMI FEMALE 12
##
## HOM/HEMI MALE 58
## -------------------------------
##
## --------------------------------------
## sickle malaria_status n
## -------- ----------------------- -----
## NORM no_malaria 318
##
## NORM assymptomatic_malaria 50
##
## NORM uncomplicated_malaria 30
##
## HET no_malaria 60
##
## HET assymptomatic_malaria 8
##
## HET uncomplicated_malaria 10
## --------------------------------------
##
## -----------------------
## sickle sex n
## -------- -------- -----
## NORM FEMALE 193
##
## NORM MALE 205
##
## HET FEMALE 40
##
## HET MALE 38
## -----------------------
##
## ------------------------------------
## thal malaria_status n
## ------ ----------------------- -----
## NORM no_malaria 125
##
## NORM assymptomatic_malaria 16
##
## NORM uncomplicated_malaria 15
##
## HET no_malaria 174
##
## HET assymptomatic_malaria 28
##
## HET uncomplicated_malaria 19
##
## HOM no_malaria 79
##
## HOM assymptomatic_malaria 14
##
## HOM uncomplicated_malaria 6
## ------------------------------------
##
## ---------------------
## thal sex n
## ------ -------- -----
## NORM FEMALE 72
##
## NORM MALE 84
##
## HET FEMALE 112
##
## HET MALE 109
##
## HOM FEMALE 49
##
## HOM MALE 50
## ---------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in individuals with g6pd activity</U></B>
##
## --------------------------------------
## g6pd_202_rtpcr thal sickle n
## ---------------- ------ -------- -----
## NORM NORM NORM 85
##
## NORM NORM HET 15
##
## NORM HET NORM 118
##
## NORM HET HET 15
##
## NORM HOM NORM 57
##
## NORM HOM HET 12
##
## HET NORM NORM 17
##
## HET NORM HET 6
##
## HET HET NORM 31
##
## HET HET HET 5
##
## HET HOM NORM 10
##
## HOM/HEMI NORM NORM 11
##
## HOM/HEMI NORM HET 1
##
## HOM/HEMI HET NORM 18
##
## HOM/HEMI HET HET 4
##
## HOM/HEMI HOM NORM 7
##
## HOM/HEMI HOM HET 1
## --------------------------------------
##
## ----------------------------------------------
## g6pd_202_rtpcr thal sickle sex n
## ---------------- ------ -------- -------- ----
## NORM NORM NORM FEMALE 31
##
## NORM NORM NORM MALE 54
##
## NORM NORM HET FEMALE 7
##
## NORM NORM HET MALE 8
##
## NORM HET NORM FEMALE 55
##
## NORM HET NORM MALE 63
##
## NORM HET HET FEMALE 9
##
## NORM HET HET MALE 6
##
## NORM HOM NORM FEMALE 27
##
## NORM HOM NORM MALE 30
##
## NORM HOM HET FEMALE 4
##
## NORM HOM HET MALE 8
##
## HET NORM NORM FEMALE 17
##
## HET NORM HET FEMALE 6
##
## HET HET NORM FEMALE 31
##
## HET HET HET FEMALE 5
##
## HET HOM NORM FEMALE 10
##
## HOM/HEMI NORM NORM FEMALE 2
##
## HOM/HEMI NORM NORM MALE 9
##
## HOM/HEMI NORM HET MALE 1
##
## HOM/HEMI HET NORM FEMALE 2
##
## HOM/HEMI HET NORM MALE 16
##
## HOM/HEMI HET HET MALE 4
##
## HOM/HEMI HOM NORM FEMALE 2
##
## HOM/HEMI HOM NORM MALE 5
##
## HOM/HEMI HOM HET FEMALE 1
## ----------------------------------------------
##
## -------------------------------------------------------------
## g6pd_202_rtpcr thal sickle malaria_status n
## ---------------- ------ -------- ----------------------- ----
## NORM NORM NORM no_malaria 70
##
## NORM NORM NORM assymptomatic_malaria 8
##
## NORM NORM NORM uncomplicated_malaria 7
##
## NORM NORM HET no_malaria 12
##
## NORM NORM HET assymptomatic_malaria 3
##
## NORM HET NORM no_malaria 96
##
## NORM HET NORM assymptomatic_malaria 13
##
## NORM HET NORM uncomplicated_malaria 9
##
## NORM HET HET no_malaria 12
##
## NORM HET HET assymptomatic_malaria 1
##
## NORM HET HET uncomplicated_malaria 2
##
## NORM HOM NORM no_malaria 43
##
## NORM HOM NORM assymptomatic_malaria 10
##
## NORM HOM NORM uncomplicated_malaria 4
##
## NORM HOM HET no_malaria 10
##
## NORM HOM HET assymptomatic_malaria 2
##
## HET NORM NORM no_malaria 14
##
## HET NORM NORM assymptomatic_malaria 2
##
## HET NORM NORM uncomplicated_malaria 1
##
## HET NORM HET no_malaria 4
##
## HET NORM HET assymptomatic_malaria 1
##
## HET NORM HET uncomplicated_malaria 1
##
## HET HET NORM no_malaria 23
##
## HET HET NORM assymptomatic_malaria 7
##
## HET HET NORM uncomplicated_malaria 1
##
## HET HET HET no_malaria 3
##
## HET HET HET assymptomatic_malaria 1
##
## HET HET HET uncomplicated_malaria 1
##
## HET HOM NORM no_malaria 9
##
## HET HOM NORM uncomplicated_malaria 1
##
## HOM/HEMI NORM NORM no_malaria 11
##
## HOM/HEMI NORM HET no_malaria 1
##
## HOM/HEMI HET NORM no_malaria 12
##
## HOM/HEMI HET NORM assymptomatic_malaria 4
##
## HOM/HEMI HET NORM uncomplicated_malaria 2
##
## HOM/HEMI HET HET no_malaria 3
##
## HOM/HEMI HET HET uncomplicated_malaria 1
##
## HOM/HEMI HOM NORM no_malaria 6
##
## HOM/HEMI HOM NORM assymptomatic_malaria 1
##
## HOM/HEMI HOM HET uncomplicated_malaria 1
## -------------------------------------------------------------
##
## -----------------------------
## g6pd_202_rtpcr thal n
## ---------------- ------ -----
## NORM NORM 100
##
## NORM HET 133
##
## NORM HOM 69
##
## HET NORM 23
##
## HET HET 36
##
## HET HOM 10
##
## HOM/HEMI NORM 12
##
## HOM/HEMI HET 22
##
## HOM/HEMI HOM 8
## -----------------------------
##
## -------------------------------------
## g6pd_202_rtpcr thal sex n
## ---------------- ------ -------- ----
## NORM NORM FEMALE 38
##
## NORM NORM MALE 62
##
## NORM HET FEMALE 64
##
## NORM HET MALE 69
##
## NORM HOM FEMALE 31
##
## NORM HOM MALE 38
##
## HET NORM FEMALE 23
##
## HET HET FEMALE 36
##
## HET HOM FEMALE 10
##
## HOM/HEMI NORM FEMALE 2
##
## HOM/HEMI NORM MALE 10
##
## HOM/HEMI HET FEMALE 2
##
## HOM/HEMI HET MALE 20
##
## HOM/HEMI HOM FEMALE 3
##
## HOM/HEMI HOM MALE 5
## -------------------------------------
##
## -----------------------------------------------------
## g6pd_202_rtpcr thal malaria_status n
## ---------------- ------ ----------------------- -----
## NORM NORM no_malaria 82
##
## NORM NORM assymptomatic_malaria 11
##
## NORM NORM uncomplicated_malaria 7
##
## NORM HET no_malaria 108
##
## NORM HET assymptomatic_malaria 14
##
## NORM HET uncomplicated_malaria 11
##
## NORM HOM no_malaria 53
##
## NORM HOM assymptomatic_malaria 12
##
## NORM HOM uncomplicated_malaria 4
##
## HET NORM no_malaria 18
##
## HET NORM assymptomatic_malaria 3
##
## HET NORM uncomplicated_malaria 2
##
## HET HET no_malaria 26
##
## HET HET assymptomatic_malaria 8
##
## HET HET uncomplicated_malaria 2
##
## HET HOM no_malaria 9
##
## HET HOM uncomplicated_malaria 1
##
## HOM/HEMI NORM no_malaria 12
##
## HOM/HEMI HET no_malaria 15
##
## HOM/HEMI HET assymptomatic_malaria 4
##
## HOM/HEMI HET uncomplicated_malaria 3
##
## HOM/HEMI HOM no_malaria 6
##
## HOM/HEMI HOM assymptomatic_malaria 1
##
## HOM/HEMI HOM uncomplicated_malaria 1
## -----------------------------------------------------
##
## -------------------------------
## g6pd_202_rtpcr sickle n
## ---------------- -------- -----
## NORM NORM 260
##
## NORM HET 42
##
## HET NORM 58
##
## HET HET 11
##
## HOM/HEMI NORM 36
##
## HOM/HEMI HET 6
## -------------------------------
##
## -------------------------------------------------------
## g6pd_202_rtpcr sickle malaria_status n
## ---------------- -------- ----------------------- -----
## NORM NORM no_malaria 209
##
## NORM NORM assymptomatic_malaria 31
##
## NORM NORM uncomplicated_malaria 20
##
## NORM HET no_malaria 34
##
## NORM HET assymptomatic_malaria 6
##
## NORM HET uncomplicated_malaria 2
##
## HET NORM no_malaria 46
##
## HET NORM assymptomatic_malaria 9
##
## HET NORM uncomplicated_malaria 3
##
## HET HET no_malaria 7
##
## HET HET assymptomatic_malaria 2
##
## HET HET uncomplicated_malaria 2
##
## HOM/HEMI NORM no_malaria 29
##
## HOM/HEMI NORM assymptomatic_malaria 5
##
## HOM/HEMI NORM uncomplicated_malaria 2
##
## HOM/HEMI HET no_malaria 4
##
## HOM/HEMI HET uncomplicated_malaria 2
## -------------------------------------------------------
##
## ----------------------------------------
## g6pd_202_rtpcr sickle sex n
## ---------------- -------- -------- -----
## NORM NORM FEMALE 113
##
## NORM NORM MALE 147
##
## NORM HET FEMALE 20
##
## NORM HET MALE 22
##
## HET NORM FEMALE 58
##
## HET HET FEMALE 11
##
## HOM/HEMI NORM FEMALE 6
##
## HOM/HEMI NORM MALE 30
##
## HOM/HEMI HET FEMALE 1
##
## HOM/HEMI HET MALE 5
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HbS genotypes in malaria negative individuals</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 256 58 54 368
##
## **HET** 48 11 9 68
##
## **Sum** 304 69 63 436
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by thal genotypes in malaria negative individuals</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 100 21 20 141
##
## **HET** 131 37 34 202
##
## **HOM** 73 11 9 93
##
## **Sum** 304 69 63 436
## ----------------------------------------
## <B><U>thal by HbS genotypes in malaria negative individuals</U></B>
##
## -----------------------------------
## NORM HET HOM Sum
## ---------- ------ ----- ----- -----
## **NORM** 117 174 77 368
##
## **HET** 24 28 16 68
##
## **Sum** 141 202 93 436
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in malaria negative individuals</U></B>
##
## --------------------------------------
## g6pd_202_rtpcr thal sickle n
## ---------------- ------ -------- -----
## NORM NORM NORM 84
##
## NORM NORM HET 16
##
## NORM HET NORM 113
##
## NORM HET HET 18
##
## NORM HOM NORM 59
##
## NORM HOM HET 14
##
## HET NORM NORM 16
##
## HET NORM HET 5
##
## HET HET NORM 31
##
## HET HET HET 6
##
## HET HOM NORM 11
##
## HOM/HEMI NORM NORM 17
##
## HOM/HEMI NORM HET 3
##
## HOM/HEMI HET NORM 30
##
## HOM/HEMI HET HET 4
##
## HOM/HEMI HOM NORM 7
##
## HOM/HEMI HOM HET 2
## --------------------------------------
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals with enzyme activity</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 100 23 12 135
##
## **HET** 133 36 22 191
##
## **HOM** 69 10 8 87
##
## **Sum** 302 69 42 413
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HBS genotypes in all individuals with enzyme activity</U></B>
##
## ----------------------------------------
## NORM HET HOM/HEMI Sum
## ---------- ------ ----- ---------- -----
## **NORM** 260 58 36 354
##
## **HET** 42 11 6 59
##
## **Sum** 302 69 42 413
## ----------------------------------------
## <B><U>thal by HbS genotypes in all individuals with enzyme activity</U></B>
##
## -----------------------------------
## NORM HET HOM Sum
## ---------- ------ ----- ----- -----
## **NORM** 113 167 74 354
##
## **HET** 22 24 13 59
##
## **Sum** 135 191 87 413
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in all individuals with enzyme activity</U></B>
##
## --------------------------------------
## g6pd_202_rtpcr thal sickle n
## ---------------- ------ -------- -----
## NORM NORM NORM 85
##
## NORM NORM HET 15
##
## NORM HET NORM 118
##
## NORM HET HET 15
##
## NORM HOM NORM 57
##
## NORM HOM HET 12
##
## HET NORM NORM 17
##
## HET NORM HET 6
##
## HET HET NORM 31
##
## HET HET HET 5
##
## HET HOM NORM 10
##
## HOM/HEMI NORM NORM 11
##
## HOM/HEMI NORM HET 1
##
## HOM/HEMI HET NORM 18
##
## HOM/HEMI HET HET 4
##
## HOM/HEMI HOM NORM 7
##
## HOM/HEMI HOM HET 1
## --------------------------------------
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.60 8.85 9.10 9.10 9.35 9.60 29
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.20 10.50 11.30 11.24 12.00 14.10 29
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_ghb3"] > 15
##
## Test Statistic: Kruskal-Wallis chi-squared = 6.929071
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.008480574
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.01640 0.01735 0.01830 0.01853 0.01960 0.02090 29
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00810 0.01000 0.01009 0.01205 0.02300 29
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_ghb3"] > 15
##
## Test Statistic: Kruskal-Wallis chi-squared = 8.453745
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.003642925
##
## FALSE TRUE
## 299 3
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 11.3 11.3 11.3 11.3 11.3 11.3 6
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.2 10.5 11.4 11.2 12.0 13.4 6
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_ghb3"] > 11
##
## Test Statistic: Kruskal-Wallis chi-squared = 0.09105572
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.7628393
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.018 0.018 0.018 0.018 0.018 0.018 6
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000500 0.005075 0.007000 0.007074 0.008750 0.014000 6
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_ghb3"] > 11
##
## Test Statistic: Kruskal-Wallis chi-squared = 2.916204
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.08769396
##
## FALSE TRUE
## 68 1
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 9.80 10.90 11.20 11.57 12.00 14.10 28
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 9.20 10.40 11.30 11.26 12.10 13.10 28
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_ghb3"] > 2.5
##
## Test Statistic: Kruskal-Wallis chi-squared = 0.1716228
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.6786735
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.004500 0.007700 0.008000 0.008867 0.011300 0.011900 28
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000400 0.000800 0.000888 0.001300 0.003000 28
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_ghb3"] > 2.5
##
## Test Statistic: Kruskal-Wallis chi-squared = 20.77818
##
## Test Statistic Parameter: df = 1
##
## P-value: 5.156729e-06
##
## FALSE TRUE
## 33 9
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.920 3.393 3.900 3.930 4.438 5.000 29
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.030 4.380 4.740 4.735 5.100 6.140 29
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_rcc"] > 350
##
## Test Statistic: Kruskal-Wallis chi-squared = 3.585663
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.05828017
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.01380 0.01650 0.01915 0.01878 0.02142 0.02300 29
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00810 0.01000 0.01006 0.01197 0.01980 29
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_rcc"] > 350
##
## Test Statistic: Kruskal-Wallis chi-squared = 10.46008
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.001219821
##
## FALSE TRUE
## 298 4
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.47 4.47 4.47 4.47 4.47 4.47 6
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.410 4.170 4.405 4.496 4.780 5.490 6
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_rcc"] > 300
##
## Test Statistic: Kruskal-Wallis chi-squared = 0.002521607
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.9599506
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.018 0.018 0.018 0.018 0.018 0.018 6
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000500 0.005075 0.007000 0.007074 0.008750 0.014000 6
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_rcc"] > 300
##
## Test Statistic: Kruskal-Wallis chi-squared = 2.916204
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.08769396
##
## FALSE TRUE
## 68 1
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.620 4.125 4.300 4.335 4.487 5.170 28
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.760 4.058 4.540 4.469 4.782 5.840 28
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_rcc"] > 100
##
## Test Statistic: Kruskal-Wallis chi-squared = 0.3705932
##
## Test Statistic Parameter: df = 1
##
## P-value: 0.5426811
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.006800 0.007775 0.009200 0.009413 0.011325 0.011900 28
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000425 0.000800 0.000994 0.001450 0.004500 28
##
## Results of Hypothesis Test
## --------------------------
##
## Alternative Hypothesis:
##
## Test Name: Kruskal-Wallis rank sum test
##
## Data: pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_rcc"] > 100
##
## Test Statistic: Kruskal-Wallis chi-squared = 19.02917
##
## Test Statistic Parameter: df = 1
##
## P-value: 1.287353e-05
##
## FALSE TRUE
## 34 8
#ASSOCIATION TESTS
##2010 Age and CBC differences as measured by kruskal walis and chisqr between the various genotype groups
##
##
## * **g6pd_202_rtpcr**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 2.281 2 0.3196
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
##
## * **thal**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 2.192 2 0.3342
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
##
## * **sickle**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.00716 1 0.9326
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
##
##
## <!-- end of list -->
##
## minus uncomplicated malaria cases
##
##
## * **g6pd_202_rtpcr**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 2.924 2 0.2318
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
##
## * **thal**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 2.759 2 0.2518
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
##
## * **sickle**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.1787 1 0.6725
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
##
##
## <!-- end of list -->
## G6PD202 and Thal combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 5.866 4 0.2094
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and sickle combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.4244 2 0.8088
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## Thal and sickle combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.1022 2 0.9502
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and Thal combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 5.817 4 0.2132
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and sickle combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.09709 2 0.9526
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## Thal and sickle combination (contigency table)
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.862 2 0.6498
## -------------------------------
##
## Table: Pearson's Chi-squared test: `.`
## association between malaria status and rbc polymorphism and cbc indices
##
##
## * **g6pd_202_rtpcr**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 0.9557 4 0.9164
## -------------------------------
##
## Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
##
## * **thal**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 1.784 4 0.7755
## -------------------------------
##
## Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
##
## * **sickle**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 2.523 2 0.2832
## -------------------------------
##
## Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
##
##
## <!-- end of list -->
##
##
## * **rbc_2010**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 1.106 2 0.5753
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
##
## * **hgb_2010**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 1.513 2 0.4692
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
##
## * **mcv_2010**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 1.137 2 0.5664
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
##
## * **mch_2010**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 1.35 2 0.5092
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
##
## * **mchc_2010**:
##
## -------------------------------
## Test statistic df P value
## ---------------- ---- ---------
## 3.371 2 0.1853
## -------------------------------
##
## Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
##
##
## <!-- end of list -->
##Univariate association tests (each cbc trait versus each polymorphism)
g6pd_202_rtpcr ++++ g6pd_202_rtpcr
rbc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.003601 |
NA |
| HOM/HEMI |
2.306e-05 |
0.2444 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 27.93 |
2 |
8.596e-07 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 14.68 |
2 |
141.7 |
1.61e-06 * * * |
hgb_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.8898 |
NA |
| HOM/HEMI |
0.8898 |
0.8898 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 0.6951 |
2 |
0.7064 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 0.8219 |
2 |
139.4 |
0.4417 |
mcv_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.003899 |
NA |
| HOM/HEMI |
1.315e-09 |
0.00836 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 44.47 |
2 |
2.201e-10 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 23.42 |
2 |
137 |
1.786e-09 * * * |
mch_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.03751 |
NA |
| HOM/HEMI |
2.928e-06 |
0.03751 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 29.07 |
2 |
4.872e-07 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 13.85 |
2 |
135.3 |
3.37e-06 * * * |
mchc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.7471 |
NA |
| HOM/HEMI |
0.7167 |
0.8042 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 1.423 |
2 |
0.4909 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 0.9746 |
2 |
133.1 |
0.38 |
thal ++++ thal
rbc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
9.891e-05 |
NA |
| HOM |
4.129e-27 |
1.766e-17 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 113.4 |
2 |
2.36e-25 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 83.62 |
2 |
266.3 |
6.578e-29 * * * |
hgb_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
0.04803 |
NA |
| HOM |
6.386e-05 |
0.009167 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 19.4 |
2 |
6.134e-05 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 10.47 |
2 |
263.1 |
4.231e-05 * * * |
mcv_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
1.58e-08 |
NA |
| HOM |
2.329e-38 |
5.189e-22 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 160.2 |
2 |
1.603e-35 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 135.9 |
2 |
270.6 |
1.382e-41 * * * |
mch_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
1.091e-09 |
NA |
| HOM |
7.195e-41 |
9.246e-23 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 169.6 |
2 |
1.494e-37 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 156.9 |
2 |
277.8 |
2.478e-46 * * * |
mchc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
| HET |
1.719e-05 |
NA |
| HOM |
2.09e-20 |
1.251e-10 |
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 88.44 |
2 |
6.254e-20 * * * |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 54.4 |
2 |
261.6 |
1.756e-20 * * * |
sickle ++++ sickle
rbc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 1.039 |
1 |
0.3082 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 0.6029 |
1 |
109.5 |
0.4391 |
hgb_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 0.227 |
1 |
0.6338 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 0.08677 |
1 |
109.4 |
0.7689 |
mcv_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 0.6876 |
1 |
0.407 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 0.4791 |
1 |
114.5 |
0.4902 |
mch_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 1.677 |
1 |
0.1953 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 1.172 |
1 |
115 |
0.2812 |
mchc_2010
pairwise t.test_____________________________________________________________
method: t tests with pooled SD
data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
p.value:
p.adjust.method: holm
tukeyHSD____________________________________________________________________
kruskal.test________________________________________________________________
Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
| 3.612 |
1 |
0.05738 |
Oneway anova________________________________________________________________
One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
| 3.901 |
1 |
119.9 |
0.05055 |
##Univariate association of CBC traits with sex, malaria and age {.tabset}
Testing for the proportion of variability in the RBC CBC indices that is accounted for by different measured factors
rbc_2010
lm__________________________________________________________________
| (Intercept) |
4.617 |
0.06198 |
74.49 |
8.701e-264 |
* * * |
| age_at_collection_years_2010 |
0.00449 |
0.007502 |
0.5985 |
0.5498 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
| 476 |
0.5355 |
0.0007552 |
-0.001353 |
| (Intercept) |
4.495 |
4.739 |
| age_at_collection_years_2010 |
-0.01025 |
0.01923 |
| (Intercept) |
4.648 |
0.0351 |
132.4 |
0 |
* * * |
| sexMALE |
0.00559 |
0.04912 |
0.1138 |
0.9094 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
| 476 |
0.5357 |
2.733e-05 |
-0.002082 |
| (Intercept) |
4.579 |
4.717 |
| sexMALE |
-0.09093 |
0.1021 |
| (Intercept) |
4.662 |
0.02754 |
169.3 |
0 |
* * * |
| malaria_statusassymptomatic_malaria |
-0.01615 |
0.0755 |
-0.2139 |
0.8307 |
|
| malaria_statusuncomplicated_malaria |
-0.1116 |
0.08902 |
-1.254 |
0.2106 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
| 476 |
0.5354 |
0.003323 |
-0.0008912 |
| (Intercept) |
4.595 |
0.0443 |
103.7 |
2.385e-297 |
* * * |
| ethnicDigo |
-0.2595 |
0.3836 |
-0.6765 |
0.4991 |
|
| ethnicDurum |
-0.1695 |
0.3836 |
-0.4419 |
0.6588 |
|
| ethnicGiriama |
0.05027 |
0.05745 |
0.8749 |
0.3821 |
|
| ethnicJibana |
0.0724 |
0.097 |
0.7463 |
0.4559 |
|
| ethnicKambe |
0.9805 |
0.3836 |
2.556 |
0.01095 |
* |
| ethnicKauma |
0.253 |
0.1956 |
1.293 |
0.1967 |
|
| ethnicRabai |
-0.1995 |
0.3836 |
-0.5201 |
0.6033 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
| 420 |
0.5389 |
0.02298 |
0.006378 |
hgb_2010
lm__________________________________________________________________
| (Intercept) |
10.2 |
0.1258 |
81.04 |
7.072e-280 |
* * * |
| age_at_collection_years_2010 |
0.1388 |
0.01523 |
9.116 |
2.208e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
| 476 |
1.087 |
0.1492 |
0.1474 |
| (Intercept) |
9.949 |
10.44 |
| age_at_collection_years_2010 |
0.1089 |
0.1688 |
| (Intercept) |
11.21 |
0.07717 |
145.3 |
0 |
* * * |
| sexMALE |
0.0744 |
0.108 |
0.6888 |
0.4913 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
| 476 |
1.178 |
0.001 |
-0.001108 |
| (Intercept) |
11.06 |
11.36 |
| sexMALE |
-0.1378 |
0.2866 |
| (Intercept) |
11.27 |
0.06062 |
185.9 |
0 |
* * * |
| malaria_statusassymptomatic_malaria |
-0.1631 |
0.1662 |
-0.9812 |
0.327 |
|
| malaria_statusuncomplicated_malaria |
0.01925 |
0.196 |
0.09821 |
0.9218 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
| 476 |
1.179 |
0.002124 |
-0.002095 |
| (Intercept) |
11.33 |
0.09416 |
120.3 |
3.953e-323 |
* * * |
| ethnicDigo |
-0.7318 |
0.8154 |
-0.8974 |
0.37 |
|
| ethnicDurum |
-1.482 |
0.8154 |
-1.817 |
0.06993 |
|
| ethnicGiriama |
0.03691 |
0.1221 |
0.3022 |
0.7626 |
|
| ethnicJibana |
-0.3343 |
0.2062 |
-1.621 |
0.1057 |
|
| ethnicKambe |
0.7682 |
0.8154 |
0.9421 |
0.3467 |
|
| ethnicKauma |
-0.8193 |
0.4158 |
-1.97 |
0.04947 |
* |
| ethnicRabai |
-0.4818 |
0.8154 |
-0.5908 |
0.555 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
| 420 |
1.146 |
0.02973 |
0.01324 |
mcv_2010
lm__________________________________________________________________
| (Intercept) |
68.45 |
0.8984 |
76.19 |
4.356e-268 |
* * * |
| age_at_collection_years_2010 |
0.6749 |
0.1087 |
6.207 |
1.179e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
| 476 |
7.762 |
0.07517 |
0.07322 |
| (Intercept) |
66.68 |
70.21 |
| age_at_collection_years_2010 |
0.4613 |
0.8886 |
| (Intercept) |
73.28 |
0.5284 |
138.7 |
0 |
* * * |
| sexMALE |
0.5675 |
0.7396 |
0.7673 |
0.4433 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
| 476 |
8.066 |
0.001241 |
-0.0008665 |
| (Intercept) |
72.24 |
74.32 |
| sexMALE |
-0.8858 |
2.021 |
| (Intercept) |
73.5 |
0.4151 |
177.1 |
0 |
* * * |
| malaria_statusassymptomatic_malaria |
-0.2909 |
1.138 |
-0.2556 |
0.7984 |
|
| malaria_statusuncomplicated_malaria |
1.296 |
1.342 |
0.9662 |
0.3345 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
| 476 |
8.071 |
0.002253 |
-0.001966 |
| (Intercept) |
74.7 |
0.6468 |
115.5 |
5.942e-316 |
* * * |
| ethnicDigo |
-1.048 |
5.601 |
-0.1871 |
0.8517 |
|
| ethnicDurum |
-6.698 |
5.601 |
-1.196 |
0.2325 |
|
| ethnicGiriama |
-0.3123 |
0.8388 |
-0.3723 |
0.7099 |
|
| ethnicJibana |
-2.603 |
1.416 |
-1.838 |
0.06678 |
|
| ethnicKambe |
-7.198 |
5.601 |
-1.285 |
0.1995 |
|
| ethnicKauma |
-6.848 |
2.856 |
-2.398 |
0.01694 |
* |
| ethnicRabai |
-3.698 |
5.601 |
-0.6602 |
0.5095 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
| 420 |
7.868 |
0.02748 |
0.01096 |
mch_2010
lm__________________________________________________________________
| (Intercept) |
22.42 |
0.3534 |
63.44 |
9.416e-234 |
* * * |
| age_at_collection_years_2010 |
0.2647 |
0.04277 |
6.188 |
1.318e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
| 476 |
3.053 |
0.07475 |
0.0728 |
| (Intercept) |
21.72 |
23.11 |
| age_at_collection_years_2010 |
0.1806 |
0.3487 |
| (Intercept) |
24.31 |
0.2078 |
117 |
0 |
* * * |
| sexMALE |
0.2385 |
0.2908 |
0.8199 |
0.4127 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
| 476 |
3.172 |
0.001416 |
-0.0006906 |
| (Intercept) |
23.9 |
24.71 |
| sexMALE |
-0.333 |
0.81 |
| (Intercept) |
24.4 |
0.1632 |
149.6 |
0 |
* * * |
| malaria_statusassymptomatic_malaria |
-0.2062 |
0.4474 |
-0.4609 |
0.6451 |
|
| malaria_statusuncomplicated_malaria |
0.5705 |
0.5275 |
1.082 |
0.28 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
| 476 |
3.173 |
0.00319 |
-0.001024 |
| (Intercept) |
24.89 |
0.2543 |
97.86 |
2.394e-287 |
* * * |
| ethnicDigo |
-0.4365 |
2.202 |
-0.1982 |
0.843 |
|
| ethnicDurum |
-2.686 |
2.202 |
-1.22 |
0.2232 |
|
| ethnicGiriama |
-0.1708 |
0.3298 |
-0.5179 |
0.6048 |
|
| ethnicJibana |
-1.069 |
0.5569 |
-1.919 |
0.0557 |
|
| ethnicKambe |
-3.236 |
2.202 |
-1.47 |
0.1425 |
|
| ethnicKauma |
-2.761 |
1.123 |
-2.459 |
0.01434 |
* |
| ethnicRabai |
-0.08649 |
2.202 |
-0.03927 |
0.9687 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
| 420 |
3.094 |
0.02878 |
0.01228 |
mchc_2010
lm__________________________________________________________________
| (Intercept) |
32.65 |
0.1229 |
265.7 |
0 |
* * * |
| age_at_collection_years_2010 |
0.06427 |
0.01487 |
4.322 |
1.886e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
| 476 |
1.061 |
0.03791 |
0.03588 |
| (Intercept) |
32.41 |
32.89 |
| age_at_collection_years_2010 |
0.03505 |
0.09349 |
| (Intercept) |
33.1 |
0.07086 |
467.1 |
0 |
* * * |
| sexMALE |
0.07293 |
0.09917 |
0.7354 |
0.4625 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
| 476 |
1.082 |
0.00114 |
-0.0009677 |
| (Intercept) |
32.96 |
33.24 |
| sexMALE |
-0.1219 |
0.2678 |
| (Intercept) |
33.13 |
0.05552 |
596.8 |
0 |
* * * |
| malaria_statusassymptomatic_malaria |
-0.1592 |
0.1522 |
-1.046 |
0.2962 |
|
| malaria_statusuncomplicated_malaria |
0.2517 |
0.1795 |
1.402 |
0.1615 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
| 476 |
1.079 |
0.007209 |
0.003011 |
| (Intercept) |
33.25 |
0.08759 |
379.6 |
0 |
* * * |
| ethnicDigo |
-0.001351 |
0.7585 |
-0.001782 |
0.9986 |
|
| ethnicDurum |
-0.5014 |
0.7585 |
-0.661 |
0.509 |
|
| ethnicGiriama |
-0.08269 |
0.1136 |
-0.7279 |
0.4671 |
|
| ethnicJibana |
-0.277 |
0.1918 |
-1.444 |
0.1494 |
|
| ethnicKambe |
-1.201 |
0.7585 |
-1.584 |
0.114 |
|
| ethnicKauma |
-0.7764 |
0.3868 |
-2.007 |
0.04538 |
* |
| ethnicRabai |
1.549 |
0.7585 |
2.042 |
0.04182 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
| 420 |
1.066 |
0.03011 |
0.01363 |
##Multivariate wambua {.tabset}
rbc_2010
####All_vs_g6pd+thal________________________________________________________________
| (Intercept) |
4.48 |
0.03921 |
114.2 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1807 |
0.05911 |
-3.057 |
0.00236 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.272 |
0.06085 |
-4.47 |
9.8e-06 |
* * * |
| thalHET |
0.2082 |
0.04827 |
4.314 |
1.956e-05 |
* * * |
| thalHOM |
0.6882 |
0.05928 |
11.61 |
1.413e-27 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
| 476 |
0.4607 |
0.2652 |
0.259 |
| (Intercept) |
4.403 |
4.557 |
| g6pd_202_rtpcrHET |
-0.2969 |
-0.06456 |
| g6pd_202_rtpcrHOM/HEMI |
-0.3916 |
-0.1524 |
| thalHET |
0.1134 |
0.3031 |
| thalHOM |
0.5717 |
0.8047 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 4.479685 0.0392144 471 4.402628 4.556742
- HET NORM 4.298978 0.0617492 471 4.177640 4.420316
- HOM/HEMI NORM 4.207659 0.0632430 471 4.083385 4.331932
- NORM HET 4.687897 0.0350109 471 4.619100 4.756694
- HET HET 4.507190 0.0567910 471 4.395595 4.618785
- HOM/HEMI HET 4.415871 0.0584286 471 4.301058 4.530684
- NORM HOM 5.167866 0.0474255 471 5.074674 5.261058
- HET HOM 4.987159 0.0689998 471 4.851573 5.122745
- HOM/HEMI HOM 4.895840 0.0710101 471 4.756304 5.035376
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

| (Intercept) |
4.475 |
0.06117 |
73.17 |
5.091e-259 |
* * * |
| g6pd_202_rtpcrHET |
-0.1806 |
0.05919 |
-3.051 |
0.002412 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.2723 |
0.06101 |
-4.464 |
1.01e-05 |
* * * |
| thalHET |
0.208 |
0.04839 |
4.298 |
2.096e-05 |
* * * |
| thalHOM |
0.6878 |
0.05949 |
11.56 |
2.208e-27 |
* * * |
| age_at_collection_years_2010 |
0.000587 |
0.006493 |
0.09041 |
0.928 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
| 476 |
0.4612 |
0.2652 |
0.2574 |
| (Intercept) |
4.355 |
4.596 |
| g6pd_202_rtpcrHET |
-0.2969 |
-0.06426 |
| g6pd_202_rtpcrHOM/HEMI |
-0.3922 |
-0.1524 |
| thalHET |
0.1129 |
0.3031 |
| thalHOM |
0.5709 |
0.8047 |
| age_at_collection_years_2010 |
-0.01217 |
0.01335 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 4.479897 0.0393260 470 4.402621 4.557174
- HET NORM 4.299329 0.0619365 470 4.177623 4.421036
- HOM/HEMI NORM 4.207563 0.0633186 470 4.083140 4.331986
- NORM HET 4.687873 0.0350488 470 4.619001 4.756744
- HET HET 4.507305 0.0568650 470 4.395563 4.619046
- HOM/HEMI HET 4.415538 0.0586060 470 4.300376 4.530700
- NORM HOM 5.167699 0.0475115 470 5.074338 5.261060
- HET HOM 4.987131 0.0690733 470 4.851400 5.122862
- HOM/HEMI HOM 4.895365 0.0712792 470 4.755299 5.035430
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

####All_vs_g6pd+sickle________________________________________________________________
| (Intercept) |
4.72 |
0.03045 |
155 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.2114 |
0.06681 |
-3.164 |
0.001659 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3116 |
0.0687 |
-4.535 |
7.308e-06 |
* * * |
| sickleHET |
0.05946 |
0.06469 |
0.9191 |
0.3585 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
| 476 |
0.5222 |
0.05391 |
0.0479 |
| (Intercept) |
4.661 |
4.78 |
| g6pd_202_rtpcrHET |
-0.3426 |
-0.08008 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4466 |
-0.1766 |
| sickleHET |
-0.06766 |
0.1866 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 4.720387 0.0304483 472 4.660556 4.780218
- HET NORM 4.509035 0.0614947 472 4.388197 4.629872
- HOM/HEMI NORM 4.408807 0.0633914 472 4.284243 4.533372
- NORM HET 4.779845 0.0616202 472 4.658761 4.900929
- HET HET 4.568492 0.0800255 472 4.411242 4.725742
- HOM/HEMI HET 4.468265 0.0822708 472 4.306602 4.629927
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
4.678 |
0.06319 |
74.02 |
1.497e-261 |
* * * |
| g6pd_202_rtpcrHET |
-0.21 |
0.06686 |
-3.14 |
0.001793 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3145 |
0.06884 |
-4.569 |
6.277e-06 |
* * * |
| sickleHET |
0.05942 |
0.06472 |
0.9181 |
0.359 |
|
| age_at_collection_years_2010 |
0.005673 |
0.007334 |
0.7735 |
0.4396 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
| 476 |
0.5224 |
0.05511 |
0.04709 |
| (Intercept) |
4.553 |
4.802 |
| g6pd_202_rtpcrHET |
-0.3414 |
-0.07859 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4497 |
-0.1792 |
| sickleHET |
-0.06775 |
0.1866 |
| age_at_collection_years_2010 |
-0.008739 |
0.02009 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 4.720604 0.0304626 471 4.660744 4.780463
- HET NORM 4.510631 0.0615555 471 4.389673 4.631588
- HOM/HEMI NORM 4.406120 0.0635135 471 4.281315 4.530925
- NORM HET 4.780021 0.0616469 471 4.658884 4.901158
- HET HET 4.570048 0.0800849 471 4.412680 4.727416
- HOM/HEMI HET 4.465537 0.0823814 471 4.303656 4.627417
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
4.73 |
0.0287 |
164.8 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.2096 |
0.06677 |
-3.139 |
0.001801 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3107 |
0.06869 |
-4.524 |
7.687e-06 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
0.5221 |
0.05222 |
0.04821 |
| (Intercept) |
4.673 |
4.786 |
| g6pd_202_rtpcrHET |
-0.3408 |
-0.07839 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4457 |
-0.1758 |
| (Intercept) |
4.687 |
0.06236 |
75.15 |
9.145e-265 |
* * * |
| g6pd_202_rtpcrHET |
-0.2082 |
0.06682 |
-3.116 |
0.001945 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3136 |
0.06882 |
-4.557 |
6.602e-06 |
* * * |
| age_at_collection_years_2010 |
0.005678 |
0.007333 |
0.7743 |
0.4391 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
0.5223 |
0.05342 |
0.0474 |
| (Intercept) |
4.564 |
4.809 |
| g6pd_202_rtpcrHET |
-0.3395 |
-0.07691 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4489 |
-0.1784 |
| age_at_collection_years_2010 |
-0.008731 |
0.02009 |
g6pd_202_rtpcr _ NORM ________________________________________________________________
| (Intercept) |
4.449 |
0.07489 |
59.4 |
2.902e-177 |
* * * |
| thalHET |
0.2765 |
0.06105 |
4.53 |
8.287e-06 |
* * * |
| thalHOM |
0.7031 |
0.07163 |
9.816 |
4.2e-20 |
* * * |
| age_at_collection_years_2010 |
-0.000304 |
0.008092 |
-0.03757 |
0.9701 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 331 |
0.4823 |
0.2281 |
0.221 |
| (Intercept) |
4.302 |
4.596 |
| thalHET |
0.1564 |
0.3966 |
| thalHOM |
0.5622 |
0.844 |
| age_at_collection_years_2010 |
-0.01622 |
0.01561 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.446688 0.0458142 327 4.356561 4.536816
- HET 4.723231 0.0403339 327 4.643884 4.802577
- HOM 5.149813 0.0549976 327 5.041619 5.258007

| (Intercept) |
4.447 |
0.04571 |
97.29 |
5.604e-244 |
* * * |
| thalHET |
0.2765 |
0.06092 |
4.538 |
7.972e-06 |
* * * |
| thalHOM |
0.703 |
0.07142 |
9.843 |
3.374e-20 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 331 |
0.4816 |
0.2281 |
0.2234 |
| (Intercept) |
4.357 |
4.537 |
| thalHET |
0.1566 |
0.3963 |
| thalHOM |
0.5625 |
0.8435 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.446757 0.0457085 328 4.356838 4.536676
- HET 4.723217 0.0402708 328 4.643995 4.802438
- HOM 5.149740 0.0548798 328 5.041779 5.257701

| (Intercept) |
4.694 |
0.07617 |
61.63 |
2.089e-182 |
* * * |
| sickleHET |
0.05799 |
0.08279 |
0.7005 |
0.4841 |
|
| age_at_collection_years_2010 |
0.003548 |
0.009183 |
0.3864 |
0.6994 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 331 |
0.5476 |
0.002024 |
-0.004061 |
| (Intercept) |
4.544 |
4.844 |
| sickleHET |
-0.1049 |
0.2209 |
| age_at_collection_years_2010 |
-0.01452 |
0.02161 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.720617 0.0327871 328 4.656118 4.785117
- HET 4.778612 0.0759970 328 4.629109 4.928115

| (Intercept) |
4.72 |
0.03274 |
144.2 |
2.106e-299 |
* * * |
| sickleHET |
0.05941 |
0.0826 |
0.7193 |
0.4725 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 331 |
0.5469 |
0.00157 |
-0.001465 |
| (Intercept) |
4.656 |
4.785 |
| sickleHET |
-0.1031 |
0.2219 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.720394 0.0327396 329 4.655989 4.784800
- HET 4.779808 0.0758357 329 4.630624 4.928992

g6pd_202_rtpcr _ HET ________________________________________________________________
| (Intercept) |
4.46 |
0.1236 |
36.09 |
2.043e-47 |
* * * |
| thalHET |
0.07402 |
0.1011 |
0.7322 |
0.4665 |
|
| thalHOM |
0.7263 |
0.1408 |
5.157 |
2.178e-06 |
* * * |
| age_at_collection_years_2010 |
-0.01309 |
0.01397 |
-0.9373 |
0.3518 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 75 |
0.3856 |
0.2991 |
0.2694 |
| (Intercept) |
4.214 |
4.707 |
| thalHET |
-0.1276 |
0.2756 |
| thalHOM |
0.4455 |
1.007 |
| age_at_collection_years_2010 |
-0.04095 |
0.01476 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.364445 0.0808341 71 4.203267 4.525624
- HET 4.438467 0.0610143 71 4.316808 4.560126
- HOM 5.090755 0.1136838 71 4.864076 5.317434

| (Intercept) |
4.372 |
0.08034 |
54.42 |
3.103e-60 |
* * * |
| thalHET |
0.06833 |
0.1008 |
0.6776 |
0.5002 |
|
| thalHOM |
0.697 |
0.1372 |
5.08 |
2.867e-06 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 75 |
0.3853 |
0.2904 |
0.2707 |
| (Intercept) |
4.212 |
4.532 |
| thalHET |
-0.1327 |
0.2693 |
| thalHOM |
0.4235 |
0.9705 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.372174 0.0803445 72 4.21201 4.532338
- HET 4.440500 0.0609242 72 4.31905 4.561950
- HOM 5.069167 0.1112319 72 4.84743 5.290903

| (Intercept) |
4.537 |
0.1356 |
33.46 |
1.292e-45 |
* * * |
| sickleHET |
-0.1458 |
0.1362 |
-1.07 |
0.288 |
|
| age_at_collection_years_2010 |
0.001428 |
0.01621 |
0.08811 |
0.93 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 75 |
0.4536 |
0.0166 |
-0.01072 |
| (Intercept) |
4.267 |
4.807 |
| sickleHET |
-0.4173 |
0.1257 |
| age_at_collection_years_2010 |
-0.03089 |
0.03375 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.547345 0.0582211 72 4.431283 4.663407
- HET 4.401568 0.1225292 72 4.157311 4.645825

| (Intercept) |
4.548 |
0.05768 |
78.84 |
2.16e-72 |
* * * |
| sickleHET |
-0.1477 |
0.1335 |
-1.106 |
0.2722 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 75 |
0.4505 |
0.01649 |
0.003018 |
| (Intercept) |
4.433 |
4.663 |
| sickleHET |
-0.4138 |
0.1184 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.547705 0.0576816 73 4.432746 4.662664
- HET 4.400000 0.1204033 73 4.160037 4.639963

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________
| (Intercept) |
4.137 |
0.1514 |
27.32 |
1.067e-37 |
* * * |
| thalHET |
0.03443 |
0.1146 |
0.3004 |
0.7648 |
|
| thalHOM |
0.6414 |
0.1616 |
3.97 |
0.0001798 |
* * * |
| age_at_collection_years_2010 |
0.02134 |
0.01614 |
1.322 |
0.1909 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 70 |
0.4236 |
0.2275 |
0.1924 |
| (Intercept) |
3.834 |
4.439 |
| thalHET |
-0.1944 |
0.2633 |
| thalHOM |
0.3189 |
0.964 |
| age_at_collection_years_2010 |
-0.0109 |
0.05357 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.308677 0.0907195 66 4.127550 4.489804
- HET 4.343109 0.0691337 66 4.205079 4.481139
- HOM 4.950097 0.1343148 66 4.681929 5.218265

| (Intercept) |
4.297 |
0.09081 |
47.32 |
3.229e-53 |
* * * |
| thalHET |
0.05589 |
0.1141 |
0.4898 |
0.6259 |
|
| thalHOM |
0.6397 |
0.1624 |
3.938 |
0.0001982 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 70 |
0.4259 |
0.2071 |
0.1834 |
| (Intercept) |
4.116 |
4.479 |
| thalHET |
-0.1719 |
0.2836 |
| thalHOM |
0.3155 |
0.964 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.297273 0.0908100 67 4.116015 4.478530
- HET 4.353158 0.0690960 67 4.215242 4.491074
- HOM 4.937000 0.1346929 67 4.668152 5.205848

| (Intercept) |
4.228 |
0.1528 |
27.66 |
2.231e-38 |
* * * |
| sickleHET |
0.2999 |
0.1462 |
2.051 |
0.04423 |
* |
| age_at_collection_years_2010 |
0.01736 |
0.01734 |
1.001 |
0.3205 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 70 |
0.461 |
0.07106 |
0.04333 |
| (Intercept) |
3.923 |
4.533 |
| sickleHET |
0.007972 |
0.5917 |
| age_at_collection_years_2010 |
-0.01726 |
0.05197 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.367596 0.0605365 67 4.246765 4.488427
- HET 4.667453 0.1331065 67 4.401771 4.933135

| (Intercept) |
4.368 |
0.06054 |
72.16 |
5.459e-66 |
* * * |
| sickleHET |
0.2969 |
0.1462 |
2.031 |
0.0462 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 70 |
0.461 |
0.05717 |
0.04331 |
| (Intercept) |
4.247 |
4.489 |
| sickleHET |
0.005147 |
0.5886 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.368104 0.0605351 68 4.247307 4.488899
- HET 4.665000 0.1330855 68 4.399432 4.930568

| (Intercept) |
4.415 |
0.03778 |
116.9 |
0 |
* * * |
| thalHET |
0.1937 |
0.04934 |
3.927 |
9.891e-05 |
* * * |
| thalHOM |
0.7038 |
0.06063 |
11.61 |
1.376e-27 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
0.4718 |
0.226 |
0.2228 |
| (Intercept) |
4.34 |
4.489 |
| thalHET |
0.09679 |
0.2907 |
| thalHOM |
0.5847 |
0.8229 |
| (Intercept) |
4.418 |
0.06126 |
72.13 |
5.167e-257 |
* * * |
| thalHET |
0.1939 |
0.04947 |
3.921 |
0.0001014 |
* * * |
| thalHOM |
0.7041 |
0.06083 |
11.57 |
1.915e-27 |
* * * |
| age_at_collection_years_2010 |
-0.0004891 |
0.006634 |
-0.07372 |
0.9413 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
0.4723 |
0.226 |
0.2211 |
| (Intercept) |
4.298 |
4.539 |
| thalHET |
0.09674 |
0.2911 |
| thalHOM |
0.5846 |
0.8237 |
| age_at_collection_years_2010 |
-0.01353 |
0.01255 |
thal _ NORM ________________________________________________________________
| (Intercept) |
4.535 |
0.1004 |
45.15 |
5.726e-90 |
* * * |
| g6pd_202_rtpcrHET |
-0.0819 |
0.114 |
-0.7188 |
0.4734 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1471 |
0.1159 |
-1.269 |
0.2063 |
|
| age_at_collection_years_2010 |
-0.01202 |
0.01211 |
-0.9931 |
0.3223 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 156 |
0.4963 |
0.0184 |
-0.0009723 |
| (Intercept) |
4.336 |
4.733 |
| g6pd_202_rtpcrHET |
-0.307 |
0.1432 |
| g6pd_202_rtpcrHOM/HEMI |
-0.376 |
0.08185 |
| age_at_collection_years_2010 |
-0.03595 |
0.0119 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.447494 0.0471165 152 4.354406 4.540581
- HET 4.365590 0.1037064 152 4.160698 4.570482
- HOM/HEMI 4.300438 0.1058683 152 4.091274 4.509601
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
4.447 |
0.04711 |
94.39 |
1.619e-137 |
* * * |
| g6pd_202_rtpcrHET |
-0.07458 |
0.1137 |
-0.6559 |
0.5129 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1495 |
0.1158 |
-1.291 |
0.1988 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 156 |
0.4963 |
0.01203 |
-0.0008819 |
| (Intercept) |
4.354 |
4.54 |
| g6pd_202_rtpcrHET |
-0.2992 |
0.1501 |
| g6pd_202_rtpcrHOM/HEMI |
-0.3783 |
0.07934 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.446757 0.0471085 153 4.353690 4.539824
- HET 4.372174 0.1034896 153 4.167721 4.576627
- HOM/HEMI 4.297273 0.1058155 153 4.088225 4.506321
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
4.488 |
0.0985 |
45.56 |
6.665e-91 |
* * * |
| sickleHET |
0.07205 |
0.1068 |
0.6745 |
0.501 |
|
| age_at_collection_years_2010 |
-0.01171 |
0.0121 |
-0.9679 |
0.3346 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 156 |
0.497 |
0.009236 |
-0.003716 |
| (Intercept) |
4.293 |
4.682 |
| sickleHET |
-0.139 |
0.2831 |
| age_at_collection_years_2010 |
-0.03561 |
0.01219 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.402671 0.0435940 153 4.316547 4.488795
- HET 4.474721 0.0975004 153 4.282100 4.667342

| (Intercept) |
4.402 |
0.04358 |
101 |
1.195e-142 |
* * * |
| sickleHET |
0.07469 |
0.1068 |
0.6997 |
0.4852 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 156 |
0.4969 |
0.003169 |
-0.003304 |
| (Intercept) |
4.316 |
4.488 |
| sickleHET |
-0.1362 |
0.2856 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.402231 0.0435827 154 4.316134 4.488328
- HET 4.476923 0.0974539 154 4.284404 4.669442

thal _ HET ________________________________________________________________
| (Intercept) |
4.698 |
0.08049 |
58.37 |
1.192e-134 |
* * * |
| g6pd_202_rtpcrHET |
-0.2813 |
0.08239 |
-3.414 |
0.0007636 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3731 |
0.08441 |
-4.42 |
1.557e-05 |
* * * |
| age_at_collection_years_2010 |
0.003257 |
0.009309 |
0.3498 |
0.7268 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 221 |
0.46 |
0.1072 |
0.09487 |
| (Intercept) |
4.54 |
4.857 |
| g6pd_202_rtpcrHET |
-0.4437 |
-0.1189 |
| g6pd_202_rtpcrHOM/HEMI |
-0.5395 |
-0.2067 |
| age_at_collection_years_2010 |
-0.01509 |
0.0216 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.723480 0.0384763 217 4.647645 4.799315
- HET 4.442209 0.0728996 217 4.298527 4.585891
- HOM/HEMI 4.350368 0.0750501 217 4.202448 4.498289
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
4.723 |
0.03839 |
123 |
2.134e-203 |
* * * |
| g6pd_202_rtpcrHET |
-0.2827 |
0.08212 |
-3.443 |
0.00069 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-0.3701 |
0.08379 |
-4.417 |
1.579e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 221 |
0.4591 |
0.1067 |
0.09851 |
| (Intercept) |
4.648 |
4.799 |
| g6pd_202_rtpcrHET |
-0.4446 |
-0.1209 |
| g6pd_202_rtpcrHOM/HEMI |
-0.5352 |
-0.2049 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.723217 0.0383914 218 4.647551 4.798883
- HET 4.440500 0.0725892 218 4.297434 4.583566
- HOM/HEMI 4.353158 0.0744749 218 4.206375 4.499941
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
4.592 |
0.08187 |
56.1 |
1.585e-131 |
* * * |
| sickleHET |
0.1055 |
0.08945 |
1.179 |
0.2396 |
|
| age_at_collection_years_2010 |
-9.892e-05 |
0.009745 |
-0.01015 |
0.9919 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 221 |
0.4842 |
0.006365 |
-0.002751 |
| (Intercept) |
4.431 |
4.754 |
| sickleHET |
-0.07081 |
0.2818 |
| age_at_collection_years_2010 |
-0.01931 |
0.01911 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.591710 0.0355178 218 4.521708 4.661712
- HET 4.697199 0.0820286 218 4.535528 4.858869

| (Intercept) |
4.592 |
0.03542 |
129.6 |
5.174e-209 |
* * * |
| sickleHET |
0.1054 |
0.08901 |
1.184 |
0.2375 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 221 |
0.4831 |
0.006365 |
0.001828 |
| (Intercept) |
4.522 |
4.662 |
| sickleHET |
-0.07 |
0.2808 |
- sickle emmean SE df lower.CL upper.CL
- NORM 4.591720 0.0354215 219 4.52191 4.661531
- HET 4.697143 0.0816563 219 4.53621 4.858076

thal _ HOM ________________________________________________________________
| (Intercept) |
5.009 |
0.1137 |
44.07 |
4.821e-65 |
* * * |
| g6pd_202_rtpcrHET |
-0.1016 |
0.1243 |
-0.8178 |
0.4155 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2066 |
0.1336 |
-1.546 |
0.1253 |
|
| age_at_collection_years_2010 |
0.01804 |
0.01339 |
1.348 |
0.181 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 99 |
0.3972 |
0.04585 |
0.01572 |
| (Intercept) |
4.784 |
5.235 |
| g6pd_202_rtpcrHET |
-0.3483 |
0.1451 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4718 |
0.05863 |
| age_at_collection_years_2010 |
-0.008536 |
0.04461 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 5.151670 0.0452885 95 5.061761 5.241579
- HET 5.050052 0.1155376 95 4.820681 5.279423
- HOM/HEMI 4.945079 0.1257506 95 4.695433 5.194726
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
5.15 |
0.04546 |
113.3 |
5.057e-104 |
* * * |
| g6pd_202_rtpcrHET |
-0.08057 |
0.1238 |
-0.6508 |
0.5167 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2127 |
0.1341 |
-1.587 |
0.1159 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 99 |
0.3989 |
0.02761 |
0.007356 |
| (Intercept) |
5.06 |
5.24 |
| g6pd_202_rtpcrHET |
-0.3263 |
0.1652 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4789 |
0.05341 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 5.149740 0.0454578 96 5.059507 5.239973
- HET 5.069167 0.1151497 96 4.840596 5.297737
- HOM/HEMI 4.937000 0.1261402 96 4.686614 5.187386
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
5.01 |
0.1171 |
42.79 |
2.469e-64 |
* * * |
| sickleHET |
-0.1031 |
0.1072 |
-0.9615 |
0.3387 |
|
| age_at_collection_years_2010 |
0.01595 |
0.01344 |
1.187 |
0.238 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 99 |
0.399 |
0.02711 |
0.006845 |
| (Intercept) |
4.778 |
5.243 |
| sickleHET |
-0.316 |
0.1097 |
| age_at_collection_years_2010 |
-0.01072 |
0.04262 |
- sickle emmean SE df lower.CL upper.CL
- NORM 5.136191 0.0441260 96 5.048601 5.223780
- HET 5.033081 0.0974523 96 4.839640 5.226522

| (Intercept) |
5.139 |
0.04415 |
116.4 |
5.298e-106 |
* * * |
| sickleHET |
-0.1196 |
0.1066 |
-1.123 |
0.2644 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 99 |
0.3998 |
0.01282 |
0.002647 |
| (Intercept) |
5.051 |
5.227 |
| sickleHET |
-0.3311 |
0.09187 |
- sickle emmean SE df lower.CL upper.CL
- NORM 5.139024 0.0441544 97 5.051390 5.226659
- HET 5.019412 0.0969743 97 4.826945 5.211879

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
| (Intercept) |
4.643 |
0.02684 |
173 |
0 |
* * * |
| sickleHET |
0.05139 |
0.0663 |
0.7751 |
0.4387 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
0.5354 |
0.001266 |
-0.0008411 |
| (Intercept) |
4.59 |
4.695 |
| sickleHET |
-0.07888 |
0.1817 |
| (Intercept) |
4.609 |
0.06295 |
73.21 |
3.64e-260 |
* * * |
| sickleHET |
0.05136 |
0.06634 |
0.7743 |
0.4392 |
|
| age_at_collection_years_2010 |
0.004487 |
0.007505 |
0.5979 |
0.5502 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
0.5358 |
0.00202 |
-0.0022 |
| (Intercept) |
4.485 |
4.732 |
| sickleHET |
-0.07899 |
0.1817 |
| age_at_collection_years_2010 |
-0.01026 |
0.01924 |
sickle _ NORM ________________________________________________________________
| (Intercept) |
4.657 |
0.06723 |
69.26 |
1.059e-222 |
* * * |
| g6pd_202_rtpcrHET |
-0.1733 |
0.07365 |
-2.353 |
0.01912 |
* |
| g6pd_202_rtpcrHOM/HEMI |
-0.3574 |
0.07535 |
-4.743 |
2.942e-06 |
* * * |
| age_at_collection_years_2010 |
0.008492 |
0.007955 |
1.068 |
0.2864 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 398 |
0.5211 |
0.06074 |
0.05358 |
| (Intercept) |
4.525 |
4.789 |
| g6pd_202_rtpcrHET |
-0.3181 |
-0.0285 |
| g6pd_202_rtpcrHOM/HEMI |
-0.5056 |
-0.2093 |
| age_at_collection_years_2010 |
-0.007148 |
0.02413 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.721235 0.0312057 394 4.659885 4.782585
- HET 4.547937 0.0667167 394 4.416772 4.679102
- HOM/HEMI 4.363815 0.0685379 394 4.229069 4.498560
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
4.72 |
0.0312 |
151.3 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1727 |
0.07366 |
-2.344 |
0.01956 |
* |
| g6pd_202_rtpcrHOM/HEMI |
-0.3523 |
0.07521 |
-4.684 |
3.874e-06 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 398 |
0.5212 |
0.05802 |
0.05325 |
| (Intercept) |
4.659 |
4.782 |
| g6pd_202_rtpcrHET |
-0.3175 |
-0.02787 |
| g6pd_202_rtpcrHOM/HEMI |
-0.5002 |
-0.2044 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.720394 0.0312012 395 4.659053 4.781735
- HET 4.547705 0.0667281 395 4.416518 4.678892
- HOM/HEMI 4.368104 0.0684321 395 4.233567 4.502640
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
4.406 |
0.06621 |
66.55 |
2.051e-216 |
* * * |
| thalHET |
0.1896 |
0.05335 |
3.555 |
0.0004241 |
* * * |
| thalHOM |
0.7372 |
0.066 |
11.17 |
2.345e-25 |
* * * |
| age_at_collection_years_2010 |
-0.0005788 |
0.00713 |
-0.08118 |
0.9353 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 398 |
0.4664 |
0.2476 |
0.2419 |
| (Intercept) |
4.276 |
4.537 |
| thalHET |
0.08476 |
0.2945 |
| thalHOM |
0.6075 |
0.867 |
| age_at_collection_years_2010 |
-0.0146 |
0.01344 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.402066 0.0409529 394 4.321553 4.482580
- HET 4.591711 0.0341955 394 4.524483 4.658940
- HOM 5.139306 0.0516183 394 5.037825 5.240788

| (Intercept) |
4.402 |
0.04085 |
107.8 |
5.556e-295 |
* * * |
| thalHET |
0.1895 |
0.05325 |
3.559 |
0.000418 |
* * * |
| thalHOM |
0.7368 |
0.06568 |
11.22 |
1.549e-25 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 398 |
0.4658 |
0.2476 |
0.2438 |
| (Intercept) |
4.322 |
4.483 |
| thalHET |
0.08481 |
0.2942 |
| thalHOM |
0.6077 |
0.8659 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.402231 0.0408512 395 4.321918 4.482544
- HET 4.591720 0.0341523 395 4.524577 4.658863
- HOM 5.139024 0.0514363 395 5.037901 5.240147

sickle _ HET ________________________________________________________________
| (Intercept) |
4.889 |
0.1667 |
29.33 |
1.716e-42 |
* * * |
| g6pd_202_rtpcrHET |
-0.403 |
0.1606 |
-2.509 |
0.0143 |
* |
| g6pd_202_rtpcrHOM/HEMI |
-0.1143 |
0.1674 |
-0.6829 |
0.4968 |
|
| age_at_collection_years_2010 |
-0.01382 |
0.01903 |
-0.7263 |
0.4699 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 78 |
0.5228 |
0.07951 |
0.0422 |
| (Intercept) |
4.557 |
5.221 |
| g6pd_202_rtpcrHET |
-0.723 |
-0.08295 |
| g6pd_202_rtpcrHOM/HEMI |
-0.448 |
0.2193 |
| age_at_collection_years_2010 |
-0.05174 |
0.02409 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.783899 0.0727201 74 4.639001 4.928797
- HET 4.380901 0.1421813 74 4.097599 4.664204
- HOM/HEMI 4.669554 0.1510545 74 4.368571 4.970537
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
4.78 |
0.07227 |
66.14 |
2.964e-68 |
* * * |
| g6pd_202_rtpcrHET |
-0.3798 |
0.1569 |
-2.42 |
0.01793 |
* |
| g6pd_202_rtpcrHOM/HEMI |
-0.1148 |
0.1669 |
-0.6879 |
0.4937 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 78 |
0.5212 |
0.07295 |
0.04823 |
| (Intercept) |
4.636 |
4.924 |
| g6pd_202_rtpcrHET |
-0.6924 |
-0.0672 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4473 |
0.2177 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 4.779808 0.0722730 75 4.635833 4.923783
- HET 4.400000 0.1392880 75 4.122524 4.677476
- HOM/HEMI 4.665000 0.1504482 75 4.365292 4.964708
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
4.509 |
0.1633 |
27.61 |
1.057e-40 |
* * * |
| thalHET |
0.2255 |
0.1327 |
1.7 |
0.0934 |
|
| thalHOM |
0.5423 |
0.1578 |
3.437 |
0.0009671 |
* * * |
| age_at_collection_years_2010 |
-0.004556 |
0.01834 |
-0.2484 |
0.8045 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 78 |
0.5058 |
0.1384 |
0.1035 |
| (Intercept) |
4.184 |
4.835 |
| thalHET |
-0.03887 |
0.4899 |
| thalHOM |
0.228 |
0.8567 |
| age_at_collection_years_2010 |
-0.0411 |
0.03199 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.474577 0.0996470 74 4.276026 4.673128
- HET 4.700107 0.0863269 74 4.528097 4.872117
- HOM 5.016898 0.1230949 74 4.771626 5.262170

| (Intercept) |
4.477 |
0.09858 |
45.42 |
2.588e-56 |
* * * |
| thalHET |
0.2202 |
0.1301 |
1.692 |
0.09476 |
|
| thalHOM |
0.5425 |
0.1568 |
3.46 |
0.0008937 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 78 |
0.5026 |
0.1377 |
0.1147 |
| (Intercept) |
4.281 |
4.673 |
| thalHET |
-0.03903 |
0.4795 |
| thalHOM |
0.2302 |
0.8548 |
- thal emmean SE df lower.CL upper.CL
- NORM 4.476923 0.0985759 75 4.280550 4.673296
- HET 4.697143 0.0849618 75 4.527890 4.866395
- HOM 5.019412 0.1219082 75 4.776558 5.262265

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
hgb_2010
####All_vs_g6pd+thal________________________________________________________________
| (Intercept) |
11.48 |
0.09862 |
116.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.0606 |
0.1486 |
-0.4077 |
0.6837 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.1318 |
0.153 |
0.861 |
0.3897 |
|
| thalHET |
-0.242 |
0.1214 |
-1.993 |
0.0468 |
* |
| thalHOM |
-0.635 |
0.1491 |
-4.259 |
2.477e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
| 476 |
1.159 |
0.03972 |
0.03157 |
| (Intercept) |
11.29 |
11.68 |
| g6pd_202_rtpcrHET |
-0.3527 |
0.2315 |
| g6pd_202_rtpcrHOM/HEMI |
-0.169 |
0.4325 |
| thalHET |
-0.4805 |
-0.003444 |
| thalHOM |
-0.9279 |
-0.342 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 11.48458 0.0986231 471 11.29079 11.67838
- HET NORM 11.42398 0.1552973 471 11.11882 11.72915
- HOM/HEMI NORM 11.61636 0.1590544 471 11.30381 11.92890
- NORM HET 11.24261 0.0880513 471 11.06959 11.41563
- HET HET 11.18201 0.1428277 471 10.90135 11.46267
- HOM/HEMI HET 11.37438 0.1469462 471 11.08563 11.66313
- NORM HOM 10.84959 0.1192737 471 10.61522 11.08396
- HET HOM 10.78899 0.1735325 471 10.44800 11.12999
- HOM/HEMI HOM 10.98136 0.1785883 471 10.63044 11.33229
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

| (Intercept) |
10.44 |
0.1406 |
74.31 |
6.107e-262 |
* * * |
| g6pd_202_rtpcrHET |
-0.02649 |
0.136 |
-0.1948 |
0.8457 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.0562 |
0.1402 |
0.4008 |
0.6887 |
|
| thalHET |
-0.3001 |
0.1112 |
-2.699 |
0.007206 |
* * |
| thalHOM |
-0.728 |
0.1367 |
-5.326 |
1.56e-07 |
* * * |
| age_at_collection_years_2010 |
0.1439 |
0.01492 |
9.645 |
3.313e-20 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
| 476 |
1.06 |
0.1984 |
0.1899 |
| (Intercept) |
10.17 |
10.72 |
| g6pd_202_rtpcrHET |
-0.2937 |
0.2408 |
| g6pd_202_rtpcrHOM/HEMI |
-0.2193 |
0.3317 |
| thalHET |
-0.5186 |
-0.0816 |
| thalHOM |
-0.9966 |
-0.4594 |
| age_at_collection_years_2010 |
0.1146 |
0.1732 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 11.53666 0.0903650 470 11.35909 11.71423
- HET NORM 11.51017 0.1423202 470 11.23050 11.78983
- HOM/HEMI NORM 11.59285 0.1454962 470 11.30695 11.87876
- NORM HET 11.23656 0.0805367 470 11.07831 11.39482
- HET HET 11.21007 0.1306668 470 10.95331 11.46684
- HOM/HEMI HET 11.29276 0.1346674 470 11.02814 11.55739
- NORM HOM 10.80862 0.1091738 470 10.59410 11.02315
- HET HOM 10.78214 0.1587194 470 10.47025 11.09402
- HOM/HEMI HOM 10.86482 0.1637883 470 10.54297 11.18667
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

####All_vs_g6pd+sickle________________________________________________________________
| (Intercept) |
11.24 |
0.06877 |
163.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.03771 |
0.1509 |
-0.25 |
0.8027 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.1626 |
0.1552 |
1.048 |
0.2953 |
|
| sickleHET |
-0.04326 |
0.1461 |
-0.2961 |
0.7673 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
| 476 |
1.179 |
0.002921 |
-0.003416 |
| (Intercept) |
11.1 |
11.37 |
| g6pd_202_rtpcrHET |
-0.3342 |
0.2588 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1423 |
0.4675 |
| sickleHET |
-0.3304 |
0.2438 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 11.23912 0.0687663 472 11.10400 11.37425
- HET NORM 11.20141 0.1388833 472 10.92850 11.47432
- HOM/HEMI NORM 11.40170 0.1431668 472 11.12038 11.68303
- NORM HET 11.19586 0.1391667 472 10.92240 11.46932
- HET HET 11.15815 0.1807344 472 10.80300 11.51329
- HOM/HEMI HET 11.35844 0.1858052 472 10.99333 11.72355
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
10.2 |
0.1318 |
77.33 |
8.022e-270 |
* * * |
| g6pd_202_rtpcrHET |
-0.004086 |
0.1395 |
-0.02929 |
0.9766 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.09183 |
0.1436 |
0.6394 |
0.5229 |
|
| sickleHET |
-0.04425 |
0.135 |
-0.3277 |
0.7433 |
|
| age_at_collection_years_2010 |
0.1382 |
0.0153 |
9.033 |
4.324e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
| 476 |
1.09 |
0.1501 |
0.1429 |
| (Intercept) |
9.937 |
10.45 |
| g6pd_202_rtpcrHET |
-0.2782 |
0.27 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1904 |
0.374 |
| sickleHET |
-0.3096 |
0.2211 |
| age_at_collection_years_2010 |
0.1082 |
0.1683 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 11.24439 0.0635570 471 11.11950 11.36928
- HET NORM 11.24030 0.1284292 471 10.98794 11.49267
- HOM/HEMI NORM 11.33622 0.1325143 471 11.07583 11.59661
- NORM HET 11.20014 0.1286197 471 10.94740 11.45288
- HET HET 11.19605 0.1670887 471 10.86772 11.52439
- HOM/HEMI HET 11.29197 0.1718802 471 10.95422 11.62972
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
11.23 |
0.06476 |
173.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.03899 |
0.1507 |
-0.2588 |
0.7959 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.162 |
0.155 |
1.045 |
0.2966 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
1.178 |
0.002736 |
-0.001481 |
| (Intercept) |
11.11 |
11.36 |
| g6pd_202_rtpcrHET |
-0.3351 |
0.2571 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1426 |
0.4665 |
| (Intercept) |
10.19 |
0.13 |
78.37 |
1.033e-272 |
* * * |
| g6pd_202_rtpcrHET |
-0.005396 |
0.1393 |
-0.03873 |
0.9691 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.0912 |
0.1435 |
0.6357 |
0.5253 |
|
| age_at_collection_years_2010 |
0.1382 |
0.01529 |
9.041 |
4.029e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
1.089 |
0.1499 |
0.1445 |
| (Intercept) |
9.933 |
10.44 |
| g6pd_202_rtpcrHET |
-0.2791 |
0.2683 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1907 |
0.3731 |
| age_at_collection_years_2010 |
0.1082 |
0.1683 |
g6pd_202_rtpcr _ NORM ________________________________________________________________
| (Intercept) |
10.3 |
0.1685 |
61.1 |
6.279e-181 |
* * * |
| thalHET |
-0.2438 |
0.1374 |
-1.774 |
0.07691 |
|
| thalHOM |
-0.6807 |
0.1612 |
-4.223 |
3.13e-05 |
* * * |
| age_at_collection_years_2010 |
0.1587 |
0.01821 |
8.718 |
1.454e-16 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 331 |
1.085 |
0.2165 |
0.2094 |
| (Intercept) |
9.966 |
10.63 |
| thalHET |
-0.5141 |
0.02648 |
| thalHOM |
-0.9978 |
-0.3636 |
| age_at_collection_years_2010 |
0.1229 |
0.1946 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.49600 0.1030997 327 11.29318 11.69882
- HET 11.25221 0.0907669 327 11.07365 11.43077
- HOM 10.81530 0.1237658 327 10.57183 11.05878

| (Intercept) |
11.46 |
0.1142 |
100.4 |
2.866e-248 |
* * * |
| thalHET |
-0.2009 |
0.1522 |
-1.32 |
0.1877 |
|
| thalHOM |
-0.6071 |
0.1784 |
-3.403 |
0.0007502 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 331 |
1.203 |
0.03446 |
0.02857 |
| (Intercept) |
11.24 |
11.68 |
| thalHET |
-0.5003 |
0.09847 |
| thalHOM |
-0.9581 |
-0.2561 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.46036 0.1141906 328 11.23572 11.68500
- HET 11.25944 0.1006060 328 11.06153 11.45735
- HOM 10.85325 0.1371029 328 10.58353 11.12296

| (Intercept) |
10.06 |
0.1548 |
64.97 |
2.302e-189 |
* * * |
| sickleHET |
0.05957 |
0.1682 |
0.354 |
0.7235 |
|
| age_at_collection_years_2010 |
0.1545 |
0.01866 |
8.279 |
3.188e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 331 |
1.113 |
0.1739 |
0.1689 |
| (Intercept) |
9.752 |
10.36 |
| sickleHET |
-0.2714 |
0.3906 |
| age_at_collection_years_2010 |
0.1178 |
0.1912 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.22297 0.0666317 328 11.09189 11.35405
- HET 11.28253 0.1544453 328 10.97871 11.58636

| (Intercept) |
11.21 |
0.07314 |
153.3 |
4.762e-308 |
* * * |
| sickleHET |
0.1214 |
0.1845 |
0.6576 |
0.5112 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 331 |
1.222 |
0.001313 |
-0.001723 |
| (Intercept) |
11.07 |
11.36 |
| sickleHET |
-0.2417 |
0.4844 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.21326 0.0731405 329 11.06938 11.35714
- HET 11.33462 0.1694175 329 11.00134 11.66789

g6pd_202_rtpcr _ HET ________________________________________________________________
| (Intercept) |
11.06 |
0.3494 |
31.64 |
1.357e-43 |
* * * |
| thalHET |
-0.5877 |
0.2859 |
-2.056 |
0.04348 |
* |
| thalHOM |
-0.6736 |
0.3983 |
-1.691 |
0.09519 |
|
| age_at_collection_years_2010 |
0.07623 |
0.03951 |
1.93 |
0.05764 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 75 |
1.091 |
0.09776 |
0.05964 |
| (Intercept) |
10.36 |
11.75 |
| thalHET |
-1.158 |
-0.01768 |
| thalHOM |
-1.468 |
0.1206 |
| age_at_collection_years_2010 |
-0.002537 |
0.155 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.61456 0.2285846 71 11.15878 12.07035
- HET 11.02683 0.1725376 71 10.68280 11.37086
- HOM 10.94097 0.3214778 71 10.29997 11.58198

| (Intercept) |
11.57 |
0.2317 |
49.94 |
1.278e-57 |
* * * |
| thalHET |
-0.5546 |
0.2907 |
-1.908 |
0.06044 |
|
| thalHOM |
-0.5029 |
0.3956 |
-1.271 |
0.2078 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 75 |
1.111 |
0.05044 |
0.02406 |
| (Intercept) |
11.11 |
12.03 |
| thalHET |
-1.134 |
0.02498 |
| thalHOM |
-1.292 |
0.2858 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.56957 0.2316533 72 11.10777 12.03136
- HET 11.01500 0.1756598 72 10.66483 11.36517
- HOM 11.06667 0.3207095 72 10.42734 11.70599

| (Intercept) |
10.91 |
0.3278 |
33.29 |
1.798e-45 |
* * * |
| sickleHET |
-0.5788 |
0.3292 |
-1.758 |
0.08299 |
|
| age_at_collection_years_2010 |
0.05312 |
0.03919 |
1.356 |
0.1795 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 75 |
1.096 |
0.07509 |
0.04939 |
| (Intercept) |
10.26 |
11.57 |
| sickleHET |
-1.235 |
0.07749 |
| age_at_collection_years_2010 |
-0.025 |
0.1312 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.30137 0.1407335 72 11.02082 11.58192
- HET 10.72260 0.2961807 72 10.13217 11.31303

| (Intercept) |
11.31 |
0.1412 |
80.14 |
6.651e-73 |
* * * |
| sickleHET |
-0.6505 |
0.3268 |
-1.99 |
0.05028 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 75 |
1.103 |
0.05148 |
0.03849 |
| (Intercept) |
11.03 |
11.6 |
| sickleHET |
-1.302 |
0.0008258 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.31475 0.1411900 73 11.03336 11.59615
- HET 10.66429 0.2947168 73 10.07692 11.25166

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________
| (Intercept) |
10.56 |
0.3153 |
33.49 |
3.867e-43 |
* * * |
| thalHET |
-0.2369 |
0.2387 |
-0.9921 |
0.3248 |
|
| thalHOM |
-0.9555 |
0.3365 |
-2.84 |
0.005993 |
* * |
| age_at_collection_years_2010 |
0.1366 |
0.03362 |
4.062 |
0.0001316 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 70 |
0.8822 |
0.28 |
0.2473 |
| (Intercept) |
9.929 |
11.19 |
| thalHET |
-0.7135 |
0.2398 |
| thalHOM |
-1.627 |
-0.2838 |
| age_at_collection_years_2010 |
0.06945 |
0.2037 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.65937 0.1889378 66 11.28214 12.03659
- HET 11.42251 0.1439818 66 11.13505 11.70998
- HOM 10.70384 0.2797318 66 10.14534 11.26234

| (Intercept) |
11.59 |
0.2087 |
55.52 |
9.483e-58 |
* * * |
| thalHET |
-0.09952 |
0.2623 |
-0.3795 |
0.7055 |
|
| thalHOM |
-0.9664 |
0.3733 |
-2.588 |
0.01181 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 70 |
0.9789 |
0.09998 |
0.07311 |
| (Intercept) |
11.17 |
12 |
| thalHET |
-0.623 |
0.4239 |
| thalHOM |
-1.712 |
-0.2212 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.58636 0.2087069 67 11.16978 12.00294
- HET 11.48684 0.1588021 67 11.16987 11.80381
- HOM 10.62000 0.3095624 67 10.00211 11.23789

| (Intercept) |
10.28 |
0.3076 |
33.44 |
1.593e-43 |
* * * |
| sickleHET |
-0.04989 |
0.2943 |
-0.1695 |
0.8659 |
|
| age_at_collection_years_2010 |
0.1388 |
0.0349 |
3.975 |
0.0001747 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 70 |
0.9278 |
0.1915 |
0.1673 |
| (Intercept) |
9.67 |
10.9 |
| sickleHET |
-0.6373 |
0.5375 |
| age_at_collection_years_2010 |
0.06909 |
0.2084 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.40284 0.1218348 67 11.15966 11.64602
- HET 11.35295 0.2678880 67 10.81824 11.88765

| (Intercept) |
11.41 |
0.1344 |
84.85 |
1.012e-70 |
* * * |
| sickleHET |
-0.07356 |
0.3247 |
-0.2266 |
0.8214 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 70 |
1.024 |
0.0007542 |
-0.01394 |
| (Intercept) |
11.14 |
11.68 |
| sickleHET |
-0.7215 |
0.5744 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.40690 0.1344396 68 11.13863 11.67517
- HET 11.33333 0.2955634 68 10.74355 11.92312

| (Intercept) |
11.49 |
0.09267 |
124 |
0 |
* * * |
| thalHET |
-0.2399 |
0.121 |
-1.982 |
0.04803 |
* |
| thalHOM |
-0.6387 |
0.1487 |
-4.294 |
2.129e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
1.157 |
0.03753 |
0.03346 |
| (Intercept) |
11.31 |
11.68 |
| thalHET |
-0.4778 |
-0.002093 |
| thalHOM |
-0.9309 |
-0.3464 |
| (Intercept) |
10.45 |
0.1372 |
76.14 |
3.031e-267 |
* * * |
| thalHET |
-0.2994 |
0.1108 |
-2.703 |
0.007116 |
* * |
| thalHOM |
-0.7299 |
0.1362 |
-5.358 |
1.321e-07 |
* * * |
| age_at_collection_years_2010 |
0.1444 |
0.01486 |
9.718 |
1.806e-20 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
1.058 |
0.198 |
0.1929 |
| (Intercept) |
10.18 |
10.72 |
| thalHET |
-0.5171 |
-0.08177 |
| thalHOM |
-0.9976 |
-0.4622 |
| age_at_collection_years_2010 |
0.1152 |
0.1736 |
thal _ NORM ________________________________________________________________
| (Intercept) |
10.33 |
0.2241 |
46.09 |
3.145e-91 |
* * * |
| g6pd_202_rtpcrHET |
0.2031 |
0.2543 |
0.7986 |
0.4258 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.09486 |
0.2586 |
0.3669 |
0.7142 |
|
| age_at_collection_years_2010 |
0.1542 |
0.02702 |
5.707 |
5.841e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 156 |
1.108 |
0.1781 |
0.1619 |
| (Intercept) |
9.888 |
10.77 |
| g6pd_202_rtpcrHET |
-0.2994 |
0.7056 |
| g6pd_202_rtpcrHOM/HEMI |
-0.416 |
0.6057 |
| age_at_collection_years_2010 |
0.1008 |
0.2076 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.45091 0.1051588 152 11.24315 11.65867
- HET 11.65401 0.2314615 152 11.19671 12.11131
- HOM/HEMI 11.54577 0.2362865 152 11.07894 12.01260
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
11.46 |
0.1155 |
99.24 |
8.673e-141 |
* * * |
| g6pd_202_rtpcrHET |
0.1092 |
0.2788 |
0.3918 |
0.6958 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.126 |
0.2839 |
0.4438 |
0.6578 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 156 |
1.217 |
0.001959 |
-0.01109 |
| (Intercept) |
11.23 |
11.69 |
| g6pd_202_rtpcrHET |
-0.4415 |
0.6599 |
| g6pd_202_rtpcrHOM/HEMI |
-0.435 |
0.687 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.46036 0.1154854 153 11.23221 11.68851
- HET 11.56957 0.2537024 153 11.06835 12.07078
- HOM/HEMI 11.58636 0.2594043 153 11.07389 12.09884
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
10.4 |
0.2192 |
47.44 |
2.087e-93 |
* * * |
| sickleHET |
-0.08782 |
0.2377 |
-0.3694 |
0.7123 |
|
| age_at_collection_years_2010 |
0.1528 |
0.02692 |
5.675 |
6.761e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 156 |
1.106 |
0.1751 |
0.1643 |
| (Intercept) |
9.966 |
10.83 |
| sickleHET |
-0.5575 |
0.3818 |
| age_at_collection_years_2010 |
0.09959 |
0.206 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.50887 0.0970235 153 11.31719 11.70055
- HET 11.42104 0.2169984 153 10.99234 11.84974

| (Intercept) |
11.51 |
0.1064 |
108.2 |
3.353e-147 |
* * * |
| sickleHET |
-0.1223 |
0.2606 |
-0.4693 |
0.6395 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 156 |
1.213 |
0.001428 |
-0.005056 |
| (Intercept) |
11.3 |
11.72 |
| sickleHET |
-0.6371 |
0.3925 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.51462 0.1063941 154 11.30443 11.72480
- HET 11.39231 0.2379045 154 10.92233 11.86229

thal _ HET ________________________________________________________________
| (Intercept) |
10.16 |
0.1879 |
54.06 |
7.12e-128 |
* * * |
| g6pd_202_rtpcrHET |
-0.1801 |
0.1924 |
-0.9364 |
0.3501 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.09155 |
0.1971 |
0.4645 |
0.6427 |
|
| age_at_collection_years_2010 |
0.1449 |
0.02173 |
6.668 |
2.116e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 221 |
1.074 |
0.1819 |
0.1705 |
| (Intercept) |
9.788 |
10.53 |
| g6pd_202_rtpcrHET |
-0.5592 |
0.199 |
| g6pd_202_rtpcrHOM/HEMI |
-0.2969 |
0.48 |
| age_at_collection_years_2010 |
0.1021 |
0.1878 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.27116 0.0898336 217 11.09410 11.44822
- HET 11.09104 0.1702044 217 10.75558 11.42651
- HOM/HEMI 11.36270 0.1752254 217 11.01734 11.70807
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
11.26 |
0.09836 |
114.5 |
1.131e-196 |
* * * |
| g6pd_202_rtpcrHET |
-0.2444 |
0.2104 |
-1.162 |
0.2466 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.2274 |
0.2147 |
1.059 |
0.2906 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 221 |
1.176 |
0.01421 |
0.005171 |
| (Intercept) |
11.07 |
11.45 |
| g6pd_202_rtpcrHET |
-0.6591 |
0.1702 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1957 |
0.6505 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.25944 0.0983633 218 11.06558 11.45331
- HET 11.01500 0.1859821 218 10.64845 11.38155
- HOM/HEMI 11.48684 0.1908136 218 11.11077 11.86292
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
10.13 |
0.1816 |
55.81 |
4.425e-131 |
* * * |
| sickleHET |
-0.1304 |
0.1984 |
-0.6575 |
0.5115 |
|
| age_at_collection_years_2010 |
0.1487 |
0.02161 |
6.88 |
6.255e-11 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 221 |
1.074 |
0.1785 |
0.1709 |
| (Intercept) |
9.776 |
10.49 |
| sickleHET |
-0.5214 |
0.2605 |
| age_at_collection_years_2010 |
0.1061 |
0.1913 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.27496 0.0787682 218 11.11971 11.43020
- HET 11.14452 0.1819157 218 10.78598 11.50305

| (Intercept) |
11.26 |
0.08666 |
129.9 |
3.213e-209 |
* * * |
| sickleHET |
-0.03057 |
0.2178 |
-0.1404 |
0.8885 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 221 |
1.182 |
8.996e-05 |
-0.004476 |
| (Intercept) |
11.09 |
11.43 |
| sickleHET |
-0.4598 |
0.3986 |
- sickle emmean SE df lower.CL upper.CL
- NORM 11.25914 0.0866643 219 11.08834 11.42994
- HET 11.22857 0.1997849 219 10.83482 11.62232

thal _ HOM ________________________________________________________________
| (Intercept) |
9.942 |
0.2749 |
36.17 |
2.378e-57 |
* * * |
| g6pd_202_rtpcrHET |
0.07696 |
0.3005 |
0.2561 |
0.7984 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1934 |
0.3231 |
-0.5986 |
0.5509 |
|
| age_at_collection_years_2010 |
0.117 |
0.03237 |
3.614 |
0.0004852 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 99 |
0.9605 |
0.1304 |
0.1029 |
| (Intercept) |
9.397 |
10.49 |
| g6pd_202_rtpcrHET |
-0.5196 |
0.6735 |
| g6pd_202_rtpcrHOM/HEMI |
-0.8347 |
0.448 |
| age_at_collection_years_2010 |
0.05271 |
0.1812 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 10.86576 0.1095163 95 10.64834 11.08318
- HET 10.94272 0.2793921 95 10.38805 11.49738
- HOM/HEMI 10.67239 0.3040892 95 10.06870 11.27608
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
10.85 |
0.1161 |
93.46 |
4.519e-96 |
* * * |
| g6pd_202_rtpcrHET |
0.2134 |
0.3163 |
0.6748 |
0.5014 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2332 |
0.3425 |
-0.6809 |
0.4976 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 99 |
1.019 |
0.01082 |
-0.009792 |
| (Intercept) |
10.62 |
11.08 |
| g6pd_202_rtpcrHET |
-0.4144 |
0.8412 |
| g6pd_202_rtpcrHOM/HEMI |
-0.9132 |
0.4467 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 10.85325 0.1161326 96 10.622725 11.08377
- HET 11.06667 0.2941772 96 10.482729 11.65060
- HOM/HEMI 10.62000 0.3222550 96 9.980329 11.25967
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
9.858 |
0.2802 |
35.18 |
1.196e-56 |
* * * |
| sickleHET |
0.1942 |
0.2567 |
0.7568 |
0.451 |
|
| age_at_collection_years_2010 |
0.1222 |
0.03216 |
3.799 |
0.0002547 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 99 |
0.955 |
0.1313 |
0.1132 |
| (Intercept) |
9.301 |
10.41 |
| sickleHET |
-0.3152 |
0.7037 |
| age_at_collection_years_2010 |
0.05834 |
0.186 |
- sickle emmean SE df lower.CL upper.CL
- NORM 10.82220 0.1056174 96 10.61255 11.03185
- HET 11.01644 0.2332561 96 10.55343 11.47945

| (Intercept) |
10.84 |
0.1125 |
96.37 |
4.063e-98 |
* * * |
| sickleHET |
0.06786 |
0.2716 |
0.2499 |
0.8032 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 99 |
1.019 |
0.0006434 |
-0.009659 |
| (Intercept) |
10.62 |
11.07 |
| sickleHET |
-0.4711 |
0.6068 |
- sickle emmean SE df lower.CL upper.CL
- NORM 10.84390 0.1125289 97 10.62056 11.06724
- HET 10.91176 0.2471421 97 10.42126 11.40227

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
| (Intercept) |
11.26 |
0.05907 |
190.6 |
0 |
* * * |
| sickleHET |
-0.04293 |
0.1459 |
-0.2942 |
0.7687 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
1.178 |
0.0001826 |
-0.001927 |
| (Intercept) |
11.14 |
11.37 |
| sickleHET |
-0.3297 |
0.2438 |
| (Intercept) |
10.2 |
0.1279 |
79.81 |
1.452e-276 |
* * * |
| sickleHET |
-0.04362 |
0.1347 |
-0.3237 |
0.7463 |
|
| age_at_collection_years_2010 |
0.1388 |
0.01524 |
9.108 |
2.374e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
1.088 |
0.1494 |
0.1458 |
| (Intercept) |
9.953 |
10.46 |
| sickleHET |
-0.3084 |
0.2212 |
| age_at_collection_years_2010 |
0.1089 |
0.1688 |
sickle _ NORM ________________________________________________________________
| (Intercept) |
10.17 |
0.1405 |
72.42 |
8.755e-230 |
* * * |
| g6pd_202_rtpcrHET |
0.09155 |
0.1539 |
0.5949 |
0.5523 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.1098 |
0.1574 |
0.6977 |
0.4858 |
|
| age_at_collection_years_2010 |
0.1387 |
0.01662 |
8.345 |
1.212e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 398 |
1.089 |
0.1534 |
0.1469 |
| (Intercept) |
9.898 |
10.45 |
| g6pd_202_rtpcrHET |
-0.211 |
0.3941 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1997 |
0.4194 |
| age_at_collection_years_2010 |
0.106 |
0.1714 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.22700 0.0652048 394 11.09880 11.35519
- HET 11.31854 0.1394058 394 11.04447 11.59262
- HOM/HEMI 11.33684 0.1432112 394 11.05529 11.61839
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
11.21 |
0.07062 |
158.8 |
0 |
* * * |
| g6pd_202_rtpcrHET |
0.1015 |
0.1667 |
0.6087 |
0.5431 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.1936 |
0.1702 |
1.137 |
0.256 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 398 |
1.18 |
0.003699 |
-0.001346 |
| (Intercept) |
11.07 |
11.35 |
| g6pd_202_rtpcrHET |
-0.2263 |
0.4293 |
| g6pd_202_rtpcrHOM/HEMI |
-0.141 |
0.5283 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.21326 0.0706214 395 11.07442 11.35210
- HET 11.31475 0.1510336 395 11.01782 11.61168
- HOM/HEMI 11.40690 0.1548904 395 11.10238 11.71141
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
10.44 |
0.1496 |
69.84 |
5.118e-224 |
* * * |
| thalHET |
-0.2948 |
0.1205 |
-2.446 |
0.01488 |
* |
| thalHOM |
-0.7838 |
0.1491 |
-5.257 |
2.4e-07 |
* * * |
| age_at_collection_years_2010 |
0.1465 |
0.01611 |
9.096 |
4.694e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 398 |
1.053 |
0.2074 |
0.2014 |
| (Intercept) |
10.15 |
10.74 |
| thalHET |
-0.5317 |
-0.05785 |
| thalHOM |
-1.077 |
-0.4907 |
| age_at_collection_years_2010 |
0.1148 |
0.1782 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.55627 0.0925067 394 11.37440 11.73814
- HET 11.26151 0.0772428 394 11.10965 11.41337
- HOM 10.77249 0.1165982 394 10.54326 11.00173

| (Intercept) |
11.51 |
0.1015 |
113.4 |
1.624e-303 |
* * * |
| thalHET |
-0.2555 |
0.1323 |
-1.931 |
0.0542 |
|
| thalHOM |
-0.6707 |
0.1632 |
-4.11 |
4.823e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 398 |
1.157 |
0.04101 |
0.03615 |
| (Intercept) |
11.32 |
11.71 |
| thalHET |
-0.5156 |
0.004629 |
| thalHOM |
-0.9916 |
-0.3498 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.51462 0.1015032 395 11.31506 11.71417
- HET 11.25914 0.0848584 395 11.09231 11.42597
- HOM 10.84390 0.1278040 395 10.59264 11.09516

sickle _ HET ________________________________________________________________
| (Intercept) |
10.35 |
0.3512 |
29.49 |
1.203e-42 |
* * * |
| g6pd_202_rtpcrHET |
-0.4618 |
0.3384 |
-1.365 |
0.1765 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.005445 |
0.3528 |
-0.01543 |
0.9877 |
|
| age_at_collection_years_2010 |
0.1243 |
0.04009 |
3.1 |
0.002739 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 78 |
1.102 |
0.1577 |
0.1236 |
| (Intercept) |
9.655 |
11.05 |
| g6pd_202_rtpcrHET |
-1.136 |
0.2125 |
| g6pd_202_rtpcrHOM/HEMI |
-0.7084 |
0.6975 |
| age_at_collection_years_2010 |
0.04439 |
0.2042 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.29783 0.1532218 74 10.99253 11.60313
- HET 10.83602 0.2995770 74 10.23910 11.43294
- HOM/HEMI 11.29238 0.3182728 74 10.65821 11.92656
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
11.33 |
0.1613 |
70.27 |
3.354e-70 |
* * * |
| g6pd_202_rtpcrHET |
-0.6703 |
0.3502 |
-1.914 |
0.05942 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.001282 |
0.3725 |
-0.003442 |
0.9973 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 78 |
1.163 |
0.04838 |
0.023 |
| (Intercept) |
11.01 |
11.66 |
| g6pd_202_rtpcrHET |
-1.368 |
0.0273 |
| g6pd_202_rtpcrHOM/HEMI |
-0.7433 |
0.7407 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 11.33462 0.1612901 75 11.01331 11.65592
- HET 10.66429 0.3108460 75 10.04505 11.28352
- HOM/HEMI 11.33333 0.3357521 75 10.66448 12.00219
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
10.41 |
0.3551 |
29.31 |
1.816e-42 |
* * * |
| thalHET |
-0.3262 |
0.2884 |
-1.131 |
0.2618 |
|
| thalHOM |
-0.4754 |
0.3429 |
-1.386 |
0.1698 |
|
| age_at_collection_years_2010 |
0.1394 |
0.03987 |
3.496 |
0.0008027 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 78 |
1.099 |
0.1609 |
0.1269 |
| (Intercept) |
9.699 |
11.11 |
| thalHET |
-0.9009 |
0.2485 |
| thalHOM |
-1.159 |
0.2079 |
| age_at_collection_years_2010 |
0.05993 |
0.2188 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.46407 0.2166006 74 11.03249 11.89566
- HET 11.13790 0.1876469 74 10.76401 11.51180
- HOM 10.98867 0.2675688 74 10.45553 11.52181

| (Intercept) |
11.39 |
0.2312 |
49.28 |
6.933e-59 |
* * * |
| thalHET |
-0.1637 |
0.3052 |
-0.5365 |
0.5932 |
|
| thalHOM |
-0.4805 |
0.3677 |
-1.307 |
0.1952 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 78 |
1.179 |
0.02239 |
-0.003681 |
| (Intercept) |
10.93 |
11.85 |
| thalHET |
-0.7718 |
0.4443 |
| thalHOM |
-1.213 |
0.2519 |
- thal emmean SE df lower.CL upper.CL
- NORM 11.39231 0.2311926 75 10.93175 11.85287
- HET 11.22857 0.1992630 75 10.83162 11.62552
- HOM 10.91176 0.2859145 75 10.34219 11.48134

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
mcv_2010
####All_vs_g6pd+thal________________________________________________________________
| (Intercept) |
76.81 |
0.5459 |
140.7 |
0 |
* * * |
| g6pd_202_rtpcrHET |
2.637 |
0.8228 |
3.205 |
0.001443 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
5.87 |
0.8472 |
6.929 |
1.396e-11 |
* * * |
| thalHET |
-4.326 |
0.6719 |
-6.438 |
2.982e-10 |
* * * |
| thalHOM |
-12.09 |
0.8252 |
-14.65 |
2.541e-40 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
| 476 |
6.413 |
0.3726 |
0.3673 |
| (Intercept) |
75.74 |
77.89 |
| g6pd_202_rtpcrHET |
1.02 |
4.254 |
| g6pd_202_rtpcrHOM/HEMI |
4.206 |
7.535 |
| thalHET |
-5.646 |
-3.006 |
| thalHOM |
-13.71 |
-10.47 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 76.81411 0.5459196 471 75.74137 77.88685
- HET NORM 79.45121 0.8596346 471 77.76202 81.14041
- HOM/HEMI NORM 82.68437 0.8804315 471 80.95431 84.41443
- NORM HET 72.48799 0.4874002 471 71.53024 73.44574
- HET HET 75.12510 0.7906102 471 73.57154 76.67866
- HOM/HEMI HET 78.35825 0.8134077 471 76.75990 79.95661
- NORM HOM 64.72275 0.6602289 471 63.42539 66.02011
- HET HOM 67.35985 0.9605738 471 65.47231 69.24739
- HOM/HEMI HOM 70.59301 0.9885599 471 68.65048 72.53555
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

| (Intercept) |
71.44 |
0.7877 |
90.69 |
6.318e-300 |
* * * |
| g6pd_202_rtpcrHET |
2.814 |
0.7622 |
3.691 |
0.0002495 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
5.479 |
0.7857 |
6.974 |
1.054e-11 |
* * * |
| thalHET |
-4.627 |
0.6232 |
-7.425 |
5.359e-13 |
* * * |
| thalHOM |
-12.57 |
0.7661 |
-16.41 |
3.569e-48 |
* * * |
| age_at_collection_years_2010 |
0.7442 |
0.08361 |
8.9 |
1.213e-17 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
| 476 |
5.939 |
0.4631 |
0.4574 |
| (Intercept) |
69.89 |
72.99 |
| g6pd_202_rtpcrHET |
1.316 |
4.311 |
| g6pd_202_rtpcrHOM/HEMI |
3.935 |
7.023 |
| thalHET |
-5.851 |
-3.402 |
| thalHOM |
-14.08 |
-11.07 |
| age_at_collection_years_2010 |
0.5799 |
0.9085 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 77.08343 0.5064576 470 76.08823 78.07863
- HET NORM 79.89694 0.7976446 470 78.32955 81.46433
- HOM/HEMI NORM 82.56282 0.8154445 470 80.96045 84.16519
- NORM HET 72.45673 0.4513742 470 71.56977 73.34369
- HET HET 75.27024 0.7323320 470 73.83119 76.70929
- HOM/HEMI HET 77.93612 0.7547539 470 76.45301 79.41923
- NORM HOM 64.51088 0.6118731 470 63.30854 65.71323
- HET HOM 67.32439 0.8895555 470 65.57639 69.07239
- HOM/HEMI HOM 69.99027 0.9179645 470 68.18645 71.79409
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

####All_vs_g6pd+sickle________________________________________________________________
| (Intercept) |
72.26 |
0.4505 |
160.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
3.1 |
0.9884 |
3.137 |
0.001815 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
6.488 |
1.016 |
6.382 |
4.172e-10 |
* * * |
| sickleHET |
-0.7936 |
0.9571 |
-0.8292 |
0.4074 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
| 476 |
7.726 |
0.08757 |
0.08177 |
| (Intercept) |
71.37 |
73.14 |
| g6pd_202_rtpcrHET |
1.158 |
5.043 |
| g6pd_202_rtpcrHOM/HEMI |
4.49 |
8.485 |
| sickleHET |
-2.674 |
1.087 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 72.25701 0.4504931 472 71.37179 73.14223
- HET NORM 75.35748 0.9098348 472 73.56965 77.14531
- HOM/HEMI NORM 78.74463 0.9378962 472 76.90166 80.58759
- NORM HET 71.46336 0.9116910 472 69.67188 73.25483
- HET HET 74.56383 1.1840040 472 72.23726 76.89040
- HOM/HEMI HET 77.95098 1.2172237 472 75.55913 80.34282
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
67.36 |
0.8994 |
74.9 |
9.204e-264 |
* * * |
| g6pd_202_rtpcrHET |
3.258 |
0.9517 |
3.424 |
0.0006717 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
6.156 |
0.9798 |
6.283 |
7.57e-10 |
* * * |
| sickleHET |
-0.7983 |
0.9212 |
-0.8666 |
0.3866 |
|
| age_at_collection_years_2010 |
0.6483 |
0.1044 |
6.21 |
1.168e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
| 476 |
7.436 |
0.1566 |
0.1494 |
| (Intercept) |
65.6 |
69.13 |
| g6pd_202_rtpcrHET |
1.388 |
5.128 |
| g6pd_202_rtpcrHOM/HEMI |
4.231 |
8.081 |
| sickleHET |
-2.608 |
1.012 |
| age_at_collection_years_2010 |
0.4431 |
0.8534 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 72.28171 0.4335908 471 71.42970 73.13372
- HET NORM 75.53989 0.8761541 471 73.81824 77.26155
- HOM/HEMI NORM 78.43753 0.9040226 471 76.66111 80.21394
- NORM HET 71.48343 0.8774539 471 69.75922 73.20764
- HET HET 74.74161 1.1398925 471 72.50171 76.98152
- HOM/HEMI HET 77.63925 1.1725800 471 75.33511 79.94338
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
72.13 |
0.4245 |
169.9 |
0 |
* * * |
| g6pd_202_rtpcrHET |
3.077 |
0.9877 |
3.115 |
0.001949 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
6.476 |
1.016 |
6.374 |
4.383e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
7.723 |
0.08624 |
0.08237 |
| (Intercept) |
71.3 |
72.97 |
| g6pd_202_rtpcrHET |
1.136 |
5.018 |
| g6pd_202_rtpcrHOM/HEMI |
4.48 |
8.473 |
| (Intercept) |
67.24 |
0.8876 |
75.75 |
2.817e-266 |
* * * |
| g6pd_202_rtpcrHET |
3.235 |
0.951 |
3.401 |
0.000728 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
6.144 |
0.9794 |
6.274 |
7.992e-10 |
* * * |
| age_at_collection_years_2010 |
0.6482 |
0.1044 |
6.211 |
1.159e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
7.434 |
0.1553 |
0.1499 |
| (Intercept) |
65.49 |
68.98 |
| g6pd_202_rtpcrHET |
1.366 |
5.103 |
| g6pd_202_rtpcrHOM/HEMI |
4.22 |
8.069 |
| age_at_collection_years_2010 |
0.4431 |
0.8533 |
g6pd_202_rtpcr _ NORM ________________________________________________________________
| (Intercept) |
71.13 |
0.9286 |
76.6 |
6.114e-211 |
* * * |
| thalHET |
-5.257 |
0.757 |
-6.944 |
2.056e-11 |
* * * |
| thalHOM |
-12.57 |
0.8881 |
-14.16 |
8.034e-36 |
* * * |
| age_at_collection_years_2010 |
0.8206 |
0.1003 |
8.178 |
6.432e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 331 |
5.98 |
0.439 |
0.4339 |
| (Intercept) |
69.31 |
72.96 |
| thalHET |
-6.746 |
-3.768 |
| thalHOM |
-14.32 |
-10.83 |
| age_at_collection_years_2010 |
0.6232 |
1.018 |
- thal emmean SE df lower.CL upper.CL
- NORM 77.32835 0.5680443 327 76.21087 78.44584
- HET 72.07171 0.5000946 327 71.08790 73.05552
- HOM 64.75452 0.6819074 327 63.41304 66.09600

| (Intercept) |
77.14 |
0.622 |
124 |
1.192e-277 |
* * * |
| thalHET |
-5.035 |
0.829 |
-6.074 |
3.457e-09 |
* * * |
| thalHOM |
-12.19 |
0.9719 |
-12.55 |
9.294e-30 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 331 |
6.553 |
0.3243 |
0.3202 |
| (Intercept) |
75.92 |
78.37 |
| thalHET |
-6.666 |
-3.404 |
| thalHOM |
-14.11 |
-10.28 |
- thal emmean SE df lower.CL upper.CL
- NORM 77.14414 0.6219989 328 75.92053 78.36775
- HET 72.10909 0.5480032 328 71.03105 73.18714
- HOM 64.95065 0.7468025 328 63.48152 66.41978

| (Intercept) |
66.53 |
1.055 |
63.07 |
1.872e-185 |
* * * |
| sickleHET |
-0.2253 |
1.147 |
-0.1966 |
0.8443 |
|
| age_at_collection_years_2010 |
0.7471 |
0.1272 |
5.875 |
1.038e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 331 |
7.583 |
0.09522 |
0.0897 |
| (Intercept) |
64.45 |
68.6 |
| sickleHET |
-2.481 |
2.03 |
| age_at_collection_years_2010 |
0.4969 |
0.9973 |
- sickle emmean SE df lower.CL upper.CL
- NORM 72.16773 0.4540489 328 71.27451 73.06094
- HET 71.94238 1.0524377 328 69.87200 74.01276

| (Intercept) |
72.12 |
0.4765 |
151.3 |
3.156e-306 |
* * * |
| sickleHET |
0.07344 |
1.202 |
0.06108 |
0.9513 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 331 |
7.96 |
1.134e-05 |
-0.003028 |
| (Intercept) |
71.18 |
73.06 |
| sickleHET |
-2.292 |
2.439 |
- sickle emmean SE df lower.CL upper.CL
- NORM 72.12079 0.4765416 329 71.18334 73.05824
- HET 72.19423 1.1038275 329 70.02278 74.36568

g6pd_202_rtpcr _ HET ________________________________________________________________
| (Intercept) |
74.96 |
1.843 |
40.68 |
6.173e-51 |
* * * |
| thalHET |
-4.123 |
1.508 |
-2.735 |
0.007878 |
* * |
| thalHOM |
-12.32 |
2.1 |
-5.866 |
1.301e-07 |
* * * |
| age_at_collection_years_2010 |
0.6055 |
0.2083 |
2.907 |
0.00487 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 75 |
5.75 |
0.3433 |
0.3156 |
| (Intercept) |
71.28 |
78.63 |
| thalHET |
-7.129 |
-1.117 |
| thalHOM |
-16.51 |
-8.131 |
| age_at_collection_years_2010 |
0.1901 |
1.021 |
- thal emmean SE df lower.CL upper.CL
- NORM 79.37914 1.2053305 71 76.97578 81.78250
- HET 75.25649 0.9097938 71 73.44241 77.07057
- HOM 67.06002 1.6951580 71 63.67997 70.44007

| (Intercept) |
79.02 |
1.26 |
62.74 |
1.36e-64 |
* * * |
| thalHET |
-3.859 |
1.581 |
-2.441 |
0.01709 |
* |
| thalHOM |
-10.96 |
2.151 |
-5.097 |
2.683e-06 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 75 |
6.041 |
0.2652 |
0.2448 |
| (Intercept) |
76.51 |
81.53 |
| thalHET |
-7.01 |
-0.7082 |
| thalHOM |
-15.25 |
-6.675 |
- thal emmean SE df lower.CL upper.CL
- NORM 79.02174 1.2595390 72 76.51089 81.53258
- HET 75.16250 0.9550927 72 73.25856 77.06644
- HOM 68.05833 1.7437527 72 64.58223 71.53444

| (Intercept) |
72.96 |
2.079 |
35.1 |
5.023e-47 |
* * * |
| sickleHET |
-0.5857 |
2.088 |
-0.2805 |
0.7799 |
|
| age_at_collection_years_2010 |
0.3229 |
0.2485 |
1.299 |
0.1981 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 75 |
6.954 |
0.02615 |
-0.0009045 |
| (Intercept) |
68.82 |
77.1 |
| sickleHET |
-4.748 |
3.576 |
| age_at_collection_years_2010 |
-0.1726 |
0.8183 |
- sickle emmean SE df lower.CL upper.CL
- NORM 75.31866 0.8925545 72 73.53939 77.09794
- HET 74.73297 1.8784252 72 70.98840 78.47754

| (Intercept) |
75.4 |
0.8945 |
84.29 |
1.733e-74 |
* * * |
| sickleHET |
-1.021 |
2.07 |
-0.4933 |
0.6233 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 75 |
6.987 |
0.003323 |
-0.01033 |
| (Intercept) |
73.62 |
77.18 |
| sickleHET |
-5.148 |
3.105 |
- sickle emmean SE df lower.CL upper.CL
- NORM 75.40000 0.8945381 73 73.61719 77.18281
- HET 74.37857 1.8672383 73 70.65717 78.09997

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________
| (Intercept) |
78.07 |
2.11 |
37 |
7.416e-46 |
* * * |
| thalHET |
-2.177 |
1.598 |
-1.362 |
0.1777 |
|
| thalHOM |
-13.43 |
2.252 |
-5.965 |
1.063e-07 |
* * * |
| age_at_collection_years_2010 |
0.4508 |
0.225 |
2.003 |
0.04923 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 70 |
5.904 |
0.3938 |
0.3662 |
| (Intercept) |
73.86 |
82.29 |
| thalHET |
-5.367 |
1.013 |
| thalHOM |
-17.93 |
-8.937 |
| age_at_collection_years_2010 |
0.001556 |
0.9001 |
- thal emmean SE df lower.CL upper.CL
- NORM 81.70915 1.2644524 66 79.18459 84.23371
- HET 79.53241 0.9635876 66 77.60854 81.45627
- HOM 68.27673 1.8720847 66 64.53899 72.01447

| (Intercept) |
81.47 |
1.287 |
63.31 |
1.669e-61 |
* * * |
| thalHET |
-1.723 |
1.617 |
-1.066 |
0.2903 |
|
| thalHOM |
-13.47 |
2.302 |
-5.851 |
1.606e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 70 |
6.035 |
0.3569 |
0.3377 |
| (Intercept) |
78.9 |
84.04 |
| thalHET |
-4.951 |
1.504 |
| thalHOM |
-18.06 |
-8.874 |
- thal emmean SE df lower.CL upper.CL
- NORM 81.46818 1.2867189 67 78.89988 84.03648
- HET 79.74474 0.9790463 67 77.79055 81.69892
- HOM 68.00000 1.9085126 67 64.19059 71.80941

| (Intercept) |
75.29 |
2.371 |
31.75 |
4.143e-42 |
* * * |
| sickleHET |
-4.27 |
2.269 |
-1.882 |
0.06422 |
|
| age_at_collection_years_2010 |
0.5021 |
0.2691 |
1.866 |
0.06643 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 70 |
7.153 |
0.09664 |
0.06968 |
| (Intercept) |
70.56 |
80.03 |
| sickleHET |
-8.798 |
0.2593 |
| age_at_collection_years_2010 |
-0.035 |
1.039 |
- sickle emmean SE df lower.CL upper.CL
- NORM 79.34049 0.9392651 67 77.46571 81.21527
- HET 75.07097 2.0652373 67 70.94873 79.19320

| (Intercept) |
79.36 |
0.9562 |
82.99 |
4.499e-70 |
* * * |
| sickleHET |
-4.355 |
2.309 |
-1.886 |
0.0636 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 70 |
7.282 |
0.0497 |
0.03572 |
| (Intercept) |
77.45 |
81.26 |
| sickleHET |
-8.964 |
0.2533 |
- sickle emmean SE df lower.CL upper.CL
- NORM 79.35517 0.9562174 68 77.44707 81.26327
- HET 75.00000 2.1022290 68 70.80507 79.19493

| (Intercept) |
78.03 |
0.5399 |
144.5 |
0 |
* * * |
| thalHET |
-4.056 |
0.7051 |
-5.753 |
1.58e-08 |
* * * |
| thalHOM |
-12.4 |
0.8664 |
-14.31 |
7.762e-39 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
6.743 |
0.3036 |
0.3006 |
| (Intercept) |
76.97 |
79.09 |
| thalHET |
-5.442 |
-2.671 |
| thalHOM |
-14.1 |
-10.69 |
| (Intercept) |
72.44 |
0.8121 |
89.2 |
1.386e-297 |
* * * |
| thalHET |
-4.373 |
0.6558 |
-6.669 |
7.237e-11 |
* * * |
| thalHOM |
-12.88 |
0.8065 |
-15.97 |
3.196e-46 |
* * * |
| age_at_collection_years_2010 |
0.7692 |
0.08796 |
8.746 |
3.928e-17 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
6.262 |
0.4007 |
0.3969 |
| (Intercept) |
70.85 |
74.04 |
| thalHET |
-5.662 |
-3.085 |
| thalHOM |
-14.47 |
-11.3 |
| age_at_collection_years_2010 |
0.5964 |
0.9421 |
thal _ NORM ________________________________________________________________
| (Intercept) |
69.91 |
1.468 |
47.62 |
3.051e-93 |
* * * |
| g6pd_202_rtpcrHET |
2.479 |
1.666 |
1.488 |
0.1388 |
|
| g6pd_202_rtpcrHOM/HEMI |
4.125 |
1.694 |
2.435 |
0.01604 |
* |
| age_at_collection_years_2010 |
0.9879 |
0.177 |
5.581 |
1.07e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 156 |
7.256 |
0.2007 |
0.1849 |
| (Intercept) |
67.01 |
72.81 |
| g6pd_202_rtpcrHET |
-0.8121 |
5.77 |
| g6pd_202_rtpcrHOM/HEMI |
0.7783 |
7.471 |
| age_at_collection_years_2010 |
0.6382 |
1.338 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 77.08360 0.6887921 152 75.72276 78.44444
- HET 79.56265 1.5160763 152 76.56735 82.55795
- HOM/HEMI 81.20817 1.5476803 152 78.15043 84.26591
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
77.14 |
0.7535 |
102.4 |
7.884e-143 |
* * * |
| g6pd_202_rtpcrHET |
1.878 |
1.819 |
1.032 |
0.3035 |
|
| g6pd_202_rtpcrHOM/HEMI |
4.324 |
1.853 |
2.334 |
0.0209 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 156 |
7.939 |
0.03693 |
0.02434 |
| (Intercept) |
75.66 |
78.63 |
| g6pd_202_rtpcrHET |
-1.716 |
5.471 |
| g6pd_202_rtpcrHOM/HEMI |
0.6639 |
7.984 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 77.14414 0.7535107 153 75.65552 78.63277
- HET 79.02174 1.6553392 153 75.75147 82.29201
- HOM/HEMI 81.46818 1.6925425 153 78.12441 84.81195
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
71.05 |
1.465 |
48.5 |
8.8e-95 |
* * * |
| sickleHET |
-0.8678 |
1.589 |
-0.5462 |
0.5857 |
|
| age_at_collection_years_2010 |
0.981 |
0.1799 |
5.452 |
1.954e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 156 |
7.393 |
0.1648 |
0.1539 |
| (Intercept) |
68.16 |
73.94 |
| sickleHET |
-4.007 |
2.271 |
| age_at_collection_years_2010 |
0.6256 |
1.336 |
- sickle emmean SE df lower.CL upper.CL
- NORM 78.1754 0.6484273 153 76.89438 79.45643
- HET 77.3076 1.4502436 153 74.44251 80.17269

| (Intercept) |
78.21 |
0.7063 |
110.7 |
1.024e-148 |
* * * |
| sickleHET |
-1.089 |
1.73 |
-0.6296 |
0.5299 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 156 |
8.053 |
0.002567 |
-0.003909 |
| (Intercept) |
76.82 |
79.61 |
| sickleHET |
-4.507 |
2.328 |
- sickle emmean SE df lower.CL upper.CL
- NORM 78.21231 0.7062822 154 76.81706 79.60756
- HET 77.12308 1.5792949 154 74.00320 80.24296

thal _ HET ________________________________________________________________
| (Intercept) |
66.7 |
0.9227 |
72.29 |
8.216e-154 |
* * * |
| g6pd_202_rtpcrHET |
3.37 |
0.9444 |
3.568 |
0.0004426 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
6.968 |
0.9676 |
7.201 |
9.671e-12 |
* * * |
| age_at_collection_years_2010 |
0.7125 |
0.1067 |
6.677 |
2.014e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 221 |
5.273 |
0.3364 |
0.3272 |
| (Intercept) |
64.88 |
68.52 |
| g6pd_202_rtpcrHET |
1.508 |
5.231 |
| g6pd_202_rtpcrHOM/HEMI |
5.061 |
8.875 |
| age_at_collection_years_2010 |
0.5022 |
0.9228 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 72.16669 0.4410558 217 71.29739 73.03599
- HET 75.53634 0.8356520 217 73.88931 77.18337
- HOM/HEMI 79.13446 0.8603034 217 77.43884 80.83008
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
72.11 |
0.483 |
149.3 |
1.706e-221 |
* * * |
| g6pd_202_rtpcrHET |
3.053 |
1.033 |
2.955 |
0.003467 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
7.636 |
1.054 |
7.243 |
7.465e-12 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 221 |
5.776 |
0.2001 |
0.1927 |
| (Intercept) |
71.16 |
73.06 |
| g6pd_202_rtpcrHET |
1.017 |
5.09 |
| g6pd_202_rtpcrHOM/HEMI |
5.558 |
9.713 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 72.10909 0.4830417 218 71.15706 73.06112
- HET 75.16250 0.9133192 218 73.36243 76.96257
- HOM/HEMI 79.74474 0.9370458 218 77.89791 81.59157
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
68.23 |
0.9898 |
68.93 |
5.455e-150 |
* * * |
| sickleHET |
-2.108 |
1.082 |
-1.949 |
0.0526 |
|
| age_at_collection_years_2010 |
0.7922 |
0.1178 |
6.723 |
1.534e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 221 |
5.854 |
0.1784 |
0.1709 |
| (Intercept) |
66.28 |
70.18 |
| sickleHET |
-4.239 |
0.02384 |
| age_at_collection_years_2010 |
0.5599 |
1.024 |
- sickle emmean SE df lower.CL upper.CL
- NORM 74.30846 0.4294234 218 73.46211 75.15481
- HET 72.20076 0.9917559 218 70.24610 74.15541

| (Intercept) |
74.22 |
0.4706 |
157.7 |
1.779e-227 |
* * * |
| sickleHET |
-1.576 |
1.182 |
-1.332 |
0.1841 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 221 |
6.418 |
0.008042 |
0.003513 |
| (Intercept) |
73.3 |
75.15 |
| sickleHET |
-3.906 |
0.7548 |
- sickle emmean SE df lower.CL upper.CL
- NORM 74.22419 0.4705697 219 73.29677 75.15162
- HET 72.64857 1.0847919 219 70.51060 74.78654

thal _ HOM ________________________________________________________________
| (Intercept) |
62.41 |
1.359 |
45.92 |
1.159e-66 |
* * * |
| g6pd_202_rtpcrHET |
2.728 |
1.486 |
1.836 |
0.06954 |
|
| g6pd_202_rtpcrHOM/HEMI |
3.16 |
1.598 |
1.978 |
0.05079 |
|
| age_at_collection_years_2010 |
0.3258 |
0.1601 |
2.035 |
0.04463 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 99 |
4.75 |
0.1067 |
0.07845 |
| (Intercept) |
59.72 |
65.11 |
| g6pd_202_rtpcrHET |
-0.2223 |
5.678 |
| g6pd_202_rtpcrHOM/HEMI |
-0.01112 |
6.332 |
| age_at_collection_years_2010 |
0.007977 |
0.6435 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 64.98550 0.5415738 95 63.91034 66.06066
- HET 67.71313 1.3816343 95 64.97024 70.45602
- HOM/HEMI 68.14591 1.5037651 95 65.16056 71.13126
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
64.95 |
0.5501 |
118.1 |
9.786e-106 |
* * * |
| g6pd_202_rtpcrHET |
3.108 |
1.498 |
2.074 |
0.04072 |
* |
| g6pd_202_rtpcrHOM/HEMI |
3.049 |
1.623 |
1.879 |
0.06323 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 99 |
4.827 |
0.06771 |
0.04829 |
| (Intercept) |
63.86 |
66.04 |
| g6pd_202_rtpcrHET |
0.134 |
6.081 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1713 |
6.27 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 64.95065 0.5500887 96 63.85873 66.04257
- HET 68.05833 1.3934375 96 65.29238 70.82428
- HOM/HEMI 68.00000 1.5264343 96 64.97005 71.02995
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
62.09 |
1.402 |
44.27 |
1.104e-65 |
* * * |
| sickleHET |
2.656 |
1.284 |
2.068 |
0.04131 |
* |
| age_at_collection_years_2010 |
0.3918 |
0.1609 |
2.434 |
0.01676 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 99 |
4.779 |
0.08616 |
0.06712 |
| (Intercept) |
59.3 |
64.87 |
| sickleHET |
0.1069 |
5.206 |
| age_at_collection_years_2010 |
0.07234 |
0.7112 |
- sickle emmean SE df lower.CL upper.CL
- NORM 65.17919 0.528528 96 64.13007 66.22831
- HET 67.83567 1.167255 96 65.51868 70.15265

| (Intercept) |
65.25 |
0.541 |
120.6 |
1.711e-107 |
* * * |
| sickleHET |
2.251 |
1.306 |
1.724 |
0.08782 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 99 |
4.899 |
0.02974 |
0.01974 |
| (Intercept) |
64.18 |
66.32 |
| sickleHET |
-0.3399 |
4.842 |
- sickle emmean SE df lower.CL upper.CL
- NORM 65.24878 0.5409907 97 64.17506 66.32250
- HET 67.50000 1.1881528 97 65.14185 69.85815

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
| (Intercept) |
73.68 |
0.4044 |
182.2 |
0 |
* * * |
| sickleHET |
-0.6597 |
0.999 |
-0.6604 |
0.5093 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
8.067 |
0.0009192 |
-0.001189 |
| (Intercept) |
72.88 |
74.47 |
| sickleHET |
-2.623 |
1.303 |
| (Intercept) |
68.56 |
0.9125 |
75.13 |
4.58e-265 |
* * * |
| sickleHET |
-0.663 |
0.9617 |
-0.6894 |
0.4909 |
|
| age_at_collection_years_2010 |
0.675 |
0.1088 |
6.204 |
1.202e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
7.766 |
0.0761 |
0.0722 |
| (Intercept) |
66.76 |
70.35 |
| sickleHET |
-2.553 |
1.227 |
| age_at_collection_years_2010 |
0.4612 |
0.8888 |
sickle _ NORM ________________________________________________________________
| (Intercept) |
67.54 |
0.9656 |
69.95 |
2.955e-224 |
* * * |
| g6pd_202_rtpcrHET |
3.235 |
1.058 |
3.059 |
0.002375 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
6.865 |
1.082 |
6.344 |
6.164e-10 |
* * * |
| age_at_collection_years_2010 |
0.6117 |
0.1143 |
5.354 |
1.463e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 398 |
7.484 |
0.1641 |
0.1577 |
| (Intercept) |
65.64 |
69.44 |
| g6pd_202_rtpcrHET |
1.156 |
5.315 |
| g6pd_202_rtpcrHOM/HEMI |
4.737 |
8.992 |
| age_at_collection_years_2010 |
0.3871 |
0.8363 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 72.18136 0.4481737 394 71.30024 73.06247
- HET 75.41671 0.9581808 394 73.53292 77.30050
- HOM/HEMI 79.04625 0.9843362 394 77.11104 80.98146
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
72.12 |
0.4635 |
155.6 |
0 |
* * * |
| g6pd_202_rtpcrHET |
3.279 |
1.094 |
2.997 |
0.002899 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
7.234 |
1.117 |
6.476 |
2.805e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 398 |
7.741 |
0.1033 |
0.09876 |
| (Intercept) |
71.21 |
73.03 |
| g6pd_202_rtpcrHET |
1.128 |
5.43 |
| g6pd_202_rtpcrHOM/HEMI |
5.038 |
9.431 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 72.12079 0.4634563 395 71.20964 73.03194
- HET 75.40000 0.9911650 395 73.45138 77.34862
- HOM/HEMI 79.35517 1.0164754 395 77.35679 81.35355
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
72.52 |
0.8829 |
82.13 |
5.722e-250 |
* * * |
| thalHET |
-4.197 |
0.7114 |
-5.9 |
7.849e-09 |
* * * |
| thalHOM |
-13.57 |
0.8801 |
-15.41 |
2.788e-42 |
* * * |
| age_at_collection_years_2010 |
0.7801 |
0.09509 |
8.204 |
3.338e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 398 |
6.219 |
0.4227 |
0.4183 |
| (Intercept) |
70.78 |
74.25 |
| thalHET |
-5.596 |
-2.799 |
| thalHOM |
-15.3 |
-11.84 |
| age_at_collection_years_2010 |
0.5931 |
0.967 |
- thal emmean SE df lower.CL upper.CL
- NORM 78.43412 0.5461445 394 77.36040 79.50784
- HET 74.23680 0.4560289 394 73.34024 75.13335
- HOM 64.86854 0.6883767 394 63.51519 66.22189

| (Intercept) |
78.21 |
0.5895 |
132.7 |
0 |
* * * |
| thalHET |
-3.988 |
0.7683 |
-5.191 |
3.362e-07 |
* * * |
| thalHOM |
-12.96 |
0.9478 |
-13.68 |
3.926e-35 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 398 |
6.721 |
0.3241 |
0.3206 |
| (Intercept) |
77.05 |
79.37 |
| thalHET |
-5.499 |
-2.478 |
| thalHOM |
-14.83 |
-11.1 |
- thal emmean SE df lower.CL upper.CL
- NORM 78.21231 0.5894791 395 77.05340 79.37122
- HET 74.22419 0.4928147 395 73.25533 75.19306
- HOM 65.24878 0.7422209 395 63.78958 66.70798

sickle _ HET ________________________________________________________________
| (Intercept) |
65.51 |
2.285 |
28.67 |
8.094e-42 |
* * * |
| g6pd_202_rtpcrHET |
3.606 |
2.202 |
1.637 |
0.1058 |
|
| g6pd_202_rtpcrHOM/HEMI |
2.777 |
2.295 |
1.21 |
0.2301 |
|
| age_at_collection_years_2010 |
0.8471 |
0.2609 |
3.247 |
0.001751 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 78 |
7.167 |
0.146 |
0.1114 |
| (Intercept) |
60.96 |
70.07 |
| g6pd_202_rtpcrHET |
-0.7819 |
7.993 |
| g6pd_202_rtpcrHOM/HEMI |
-1.796 |
7.351 |
| age_at_collection_years_2010 |
0.3274 |
1.367 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 71.94347 0.9969432 74 69.95702 73.92992
- HET 75.54923 1.9492083 74 71.66534 79.43311
- HOM/HEMI 74.72086 2.0708532 74 70.59460 78.84713
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
72.19 |
1.055 |
68.41 |
2.444e-69 |
* * * |
| g6pd_202_rtpcrHET |
2.184 |
2.291 |
0.9533 |
0.3435 |
|
| g6pd_202_rtpcrHOM/HEMI |
2.806 |
2.437 |
1.151 |
0.2533 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 78 |
7.61 |
0.02434 |
-0.00168 |
| (Intercept) |
70.09 |
74.3 |
| g6pd_202_rtpcrHET |
-2.38 |
6.749 |
| g6pd_202_rtpcrHOM/HEMI |
-2.049 |
7.661 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 72.19423 1.055310 75 70.09194 74.29652
- HET 74.37857 2.033845 75 70.32694 78.43020
- HOM/HEMI 75.00000 2.196804 75 70.62374 79.37626
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
71.36 |
2.051 |
34.79 |
1.325e-47 |
* * * |
| thalHET |
-5.425 |
1.666 |
-3.255 |
0.00171 |
* * |
| thalHOM |
-9.593 |
1.981 |
-4.842 |
6.862e-06 |
* * * |
| age_at_collection_years_2010 |
0.8151 |
0.2303 |
3.539 |
0.0006983 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 78 |
6.352 |
0.3293 |
0.3021 |
| (Intercept) |
67.27 |
75.44 |
| thalHET |
-8.745 |
-2.104 |
| thalHOM |
-13.54 |
-5.645 |
| age_at_collection_years_2010 |
0.3562 |
1.274 |
- thal emmean SE df lower.CL upper.CL
- NORM 77.54282 1.251378 74 75.04939 80.03624
- HET 72.11828 1.084103 74 69.95816 74.27840
- HOM 67.94981 1.545839 74 64.86966 71.02997

| (Intercept) |
77.12 |
1.338 |
57.64 |
7.33e-64 |
* * * |
| thalHET |
-4.475 |
1.766 |
-2.533 |
0.0134 |
* |
| thalHOM |
-9.623 |
2.128 |
-4.522 |
2.25e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 78 |
6.823 |
0.2158 |
0.1949 |
| (Intercept) |
74.46 |
79.79 |
| thalHET |
-7.993 |
-0.9556 |
| thalHOM |
-13.86 |
-5.384 |
- thal emmean SE df lower.CL upper.CL
- NORM 77.12308 1.338034 75 74.45758 79.78858
- HET 72.64857 1.153240 75 70.35120 74.94594
- HOM 67.50000 1.654739 75 64.20359 70.79641

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
mch_2010
####All_vs_g6pd+thal________________________________________________________________
| (Intercept) |
25.9 |
0.2168 |
119.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
0.7558 |
0.3268 |
2.313 |
0.02117 |
* |
| g6pd_202_rtpcrHOM/HEMI |
1.774 |
0.3365 |
5.272 |
2.061e-07 |
* * * |
| thalHET |
-1.785 |
0.2669 |
-6.688 |
6.423e-11 |
* * * |
| thalHOM |
-4.92 |
0.3278 |
-15.01 |
6.801e-42 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
| 476 |
2.547 |
0.36 |
0.3546 |
| (Intercept) |
25.47 |
26.33 |
| g6pd_202_rtpcrHET |
0.1136 |
1.398 |
| g6pd_202_rtpcrHOM/HEMI |
1.113 |
2.435 |
| thalHET |
-2.309 |
-1.261 |
| thalHOM |
-5.564 |
-4.275 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 25.89929 0.2168404 471 25.47319 26.32538
- HET NORM 26.65514 0.3414487 471 25.98419 27.32609
- HOM/HEMI NORM 27.67323 0.3497092 471 26.98605 28.36041
- NORM HET 24.11428 0.1935964 471 23.73386 24.49470
- HET HET 24.87013 0.3140320 471 24.25305 25.48721
- HOM/HEMI HET 25.88822 0.3230872 471 25.25335 26.52309
- NORM HOM 20.97970 0.2622443 471 20.46439 21.49501
- HET HOM 21.73555 0.3815419 471 20.98582 22.48529
- HOM/HEMI HOM 22.75364 0.3926580 471 21.98206 23.52522
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

| (Intercept) |
23.76 |
0.3129 |
75.95 |
4.833e-266 |
* * * |
| g6pd_202_rtpcrHET |
0.8259 |
0.3028 |
2.728 |
0.006612 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
1.619 |
0.3121 |
5.186 |
3.192e-07 |
* * * |
| thalHET |
-1.904 |
0.2475 |
-7.694 |
8.461e-14 |
* * * |
| thalHOM |
-5.111 |
0.3043 |
-16.79 |
6.319e-50 |
* * * |
| age_at_collection_years_2010 |
0.2956 |
0.03321 |
8.901 |
1.211e-17 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
| 476 |
2.359 |
0.4524 |
0.4465 |
| (Intercept) |
23.15 |
24.38 |
| g6pd_202_rtpcrHET |
0.231 |
1.421 |
| g6pd_202_rtpcrHOM/HEMI |
1.005 |
2.232 |
| thalHET |
-2.391 |
-1.418 |
| thalHOM |
-5.709 |
-4.513 |
| age_at_collection_years_2010 |
0.2303 |
0.3609 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 26.00626 0.2011656 470 25.61097 26.40156
- HET NORM 26.83218 0.3168254 470 26.20961 27.45475
- HOM/HEMI NORM 27.62495 0.3238955 470 26.98849 28.26141
- NORM HET 24.10186 0.1792864 470 23.74956 24.45417
- HET HET 24.92778 0.2908831 470 24.35619 25.49937
- HOM/HEMI HET 25.72055 0.2997891 470 25.13146 26.30964
- NORM HOM 20.89555 0.2430367 470 20.41797 21.37312
- HET HOM 21.72146 0.3533325 470 21.02716 22.41577
- HOM/HEMI HOM 22.51423 0.3646166 470 21.79775 23.23071
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

####All_vs_g6pd+sickle________________________________________________________________
| (Intercept) |
24.05 |
0.1802 |
133.5 |
0 |
* * * |
| g6pd_202_rtpcrHET |
0.9455 |
0.3953 |
2.392 |
0.01716 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.024 |
0.4065 |
4.979 |
8.982e-07 |
* * * |
| sickleHET |
-0.4449 |
0.3828 |
-1.162 |
0.2457 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
| 476 |
3.09 |
0.05641 |
0.05041 |
| (Intercept) |
23.7 |
24.41 |
| g6pd_202_rtpcrHET |
0.1687 |
1.722 |
| g6pd_202_rtpcrHOM/HEMI |
1.225 |
2.823 |
| sickleHET |
-1.197 |
0.3072 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 24.05359 0.1801724 472 23.69955 24.40763
- HET NORM 24.99906 0.3638839 472 24.28402 25.71409
- HOM/HEMI NORM 26.07770 0.3751069 472 25.34062 26.81479
- NORM HET 23.60864 0.3646263 472 22.89215 24.32513
- HET HET 24.55411 0.4735365 472 23.62361 25.48461
- HOM/HEMI HET 25.63276 0.4868225 472 24.67615 26.58937
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
22.12 |
0.36 |
61.43 |
5.343e-227 |
* * * |
| g6pd_202_rtpcrHET |
1.008 |
0.3809 |
2.646 |
0.008423 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
1.893 |
0.3922 |
4.826 |
1.883e-06 |
* * * |
| sickleHET |
-0.4468 |
0.3687 |
-1.212 |
0.2262 |
|
| age_at_collection_years_2010 |
0.2565 |
0.04179 |
6.139 |
1.768e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
| 476 |
2.976 |
0.1263 |
0.1189 |
| (Intercept) |
21.41 |
22.82 |
| g6pd_202_rtpcrHET |
0.2593 |
1.756 |
| g6pd_202_rtpcrHOM/HEMI |
1.122 |
2.663 |
| sickleHET |
-1.171 |
0.2778 |
| age_at_collection_years_2010 |
0.1744 |
0.3386 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 24.06336 0.1735616 471 23.72231 24.40441
- HET NORM 25.07124 0.3507148 471 24.38208 25.76040
- HOM/HEMI NORM 25.95618 0.3618703 471 25.24510 26.66726
- NORM HET 23.61658 0.3512352 471 22.92640 24.30677
- HET HET 24.62446 0.4562864 471 23.72785 25.52107
- HOM/HEMI HET 25.50940 0.4693709 471 24.58708 26.43172
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
23.98 |
0.1699 |
141.2 |
0 |
* * * |
| g6pd_202_rtpcrHET |
0.9323 |
0.3953 |
2.358 |
0.01876 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.018 |
0.4067 |
4.962 |
9.759e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
3.091 |
0.05371 |
0.0497 |
| (Intercept) |
23.65 |
24.32 |
| g6pd_202_rtpcrHET |
0.1555 |
1.709 |
| g6pd_202_rtpcrHOM/HEMI |
1.219 |
2.817 |
| (Intercept) |
22.05 |
0.3556 |
62.01 |
5.493e-229 |
* * * |
| g6pd_202_rtpcrHET |
0.9947 |
0.381 |
2.611 |
0.009319 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
1.886 |
0.3923 |
4.808 |
2.053e-06 |
* * * |
| age_at_collection_years_2010 |
0.2565 |
0.04181 |
6.135 |
1.807e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
2.978 |
0.1236 |
0.118 |
| (Intercept) |
21.35 |
22.75 |
| g6pd_202_rtpcrHET |
0.2461 |
1.743 |
| g6pd_202_rtpcrHOM/HEMI |
1.115 |
2.657 |
| age_at_collection_years_2010 |
0.1743 |
0.3386 |
g6pd_202_rtpcr _ NORM ________________________________________________________________
| (Intercept) |
23.6 |
0.3676 |
64.21 |
1.909e-187 |
* * * |
| thalHET |
-2.115 |
0.2996 |
-7.059 |
1.01e-11 |
* * * |
| thalHOM |
-5.032 |
0.3515 |
-14.31 |
2.03e-36 |
* * * |
| age_at_collection_years_2010 |
0.3267 |
0.03971 |
8.226 |
4.622e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 331 |
2.367 |
0.4438 |
0.4387 |
| (Intercept) |
22.88 |
24.32 |
| thalHET |
-2.704 |
-1.526 |
| thalHOM |
-5.723 |
-4.34 |
| age_at_collection_years_2010 |
0.2486 |
0.4048 |
- thal emmean SE df lower.CL upper.CL
- NORM 26.06794 0.2248432 327 25.62561 26.51026
- HET 23.95295 0.1979473 327 23.56354 24.34236
- HOM 21.03620 0.2699124 327 20.50522 21.56718

| (Intercept) |
25.99 |
0.2464 |
105.5 |
3.857e-255 |
* * * |
| thalHET |
-2.027 |
0.3284 |
-6.171 |
2.003e-09 |
* * * |
| thalHOM |
-4.88 |
0.3851 |
-12.67 |
3.128e-30 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 331 |
2.596 |
0.3287 |
0.3246 |
| (Intercept) |
25.51 |
26.48 |
| thalHET |
-2.673 |
-1.381 |
| thalHOM |
-5.638 |
-4.123 |
- thal emmean SE df lower.CL upper.CL
- NORM 25.99459 0.2464449 328 25.50978 26.47941
- HET 23.96783 0.2171267 328 23.54070 24.39497
- HOM 21.11429 0.2958939 328 20.53220 21.69637

| (Intercept) |
21.77 |
0.4192 |
51.93 |
2.737e-160 |
* * * |
| sickleHET |
-0.2024 |
0.4556 |
-0.4443 |
0.6571 |
|
| age_at_collection_years_2010 |
0.2978 |
0.05053 |
5.894 |
9.379e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 331 |
3.013 |
0.09584 |
0.09033 |
| (Intercept) |
20.94 |
22.59 |
| sickleHET |
-1.099 |
0.6939 |
| age_at_collection_years_2010 |
0.1984 |
0.3972 |
- sickle emmean SE df lower.CL upper.CL
- NORM 24.01549 0.1804346 328 23.66053 24.37044
- HET 23.81306 0.4182285 328 22.99031 24.63581

| (Intercept) |
24 |
0.1894 |
126.7 |
3.067e-281 |
* * * |
| sickleHET |
-0.08331 |
0.4779 |
-0.1743 |
0.8617 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 331 |
3.164 |
9.236e-05 |
-0.002947 |
| (Intercept) |
23.62 |
24.37 |
| sickleHET |
-1.023 |
0.8569 |
- sickle emmean SE df lower.CL upper.CL
- NORM 23.99677 0.1894301 329 23.62413 24.36942
- HET 23.91346 0.4387827 329 23.05029 24.77664

g6pd_202_rtpcr _ HET ________________________________________________________________
| (Intercept) |
25 |
0.7427 |
33.66 |
2.22e-45 |
* * * |
| thalHET |
-1.889 |
0.6077 |
-3.109 |
0.002699 |
* * |
| thalHOM |
-5.254 |
0.8466 |
-6.207 |
3.224e-08 |
* * * |
| age_at_collection_years_2010 |
0.2416 |
0.08397 |
2.878 |
0.00529 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 75 |
2.318 |
0.366 |
0.3392 |
| (Intercept) |
23.52 |
26.48 |
| thalHET |
-3.101 |
-0.6777 |
| thalHOM |
-6.942 |
-3.566 |
| age_at_collection_years_2010 |
0.07419 |
0.409 |
- thal emmean SE df lower.CL upper.CL
- NORM 26.76436 0.4858482 71 25.79560 27.73311
- HET 24.87501 0.3667224 71 24.14378 25.60623
- HOM 21.50996 0.6832894 71 20.14752 22.87240

| (Intercept) |
26.62 |
0.5072 |
52.49 |
3.902e-59 |
* * * |
| thalHET |
-1.784 |
0.6365 |
-2.803 |
0.006496 |
* * |
| thalHOM |
-4.713 |
0.8661 |
-5.442 |
6.917e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 75 |
2.432 |
0.2921 |
0.2724 |
| (Intercept) |
25.61 |
27.63 |
| thalHET |
-3.053 |
-0.5154 |
| thalHOM |
-6.44 |
-2.987 |
- thal emmean SE df lower.CL upper.CL
- NORM 26.62174 0.5071601 72 25.61073 27.63274
- HET 24.83750 0.3845732 72 24.07087 25.60413
- HOM 21.90833 0.7021314 72 20.50866 23.30801

| (Intercept) |
24.14 |
0.8532 |
28.29 |
9.694e-41 |
* * * |
| sickleHET |
-0.4575 |
0.8569 |
-0.5339 |
0.5951 |
|
| age_at_collection_years_2010 |
0.1181 |
0.102 |
1.158 |
0.2507 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 75 |
2.854 |
0.02535 |
-0.001722 |
| (Intercept) |
22.44 |
25.84 |
| sickleHET |
-2.166 |
1.251 |
| age_at_collection_years_2010 |
-0.08522 |
0.3214 |
- sickle emmean SE df lower.CL upper.CL
- NORM 25.00139 0.3663026 72 24.27118 25.7316
- HET 24.54393 0.7709020 72 23.00717 26.0807

| (Intercept) |
25.03 |
0.3663 |
68.34 |
6.373e-68 |
* * * |
| sickleHET |
-0.6169 |
0.8477 |
-0.7277 |
0.4691 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 75 |
2.861 |
0.007201 |
-0.006399 |
| (Intercept) |
24.3 |
25.76 |
| sickleHET |
-2.306 |
1.073 |
- sickle emmean SE df lower.CL upper.CL
- NORM 25.03115 0.3662521 73 24.30121 25.76109
- HET 24.41429 0.7645061 73 22.89063 25.93794

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________
| (Intercept) |
25.91 |
0.8342 |
31.06 |
4.096e-41 |
* * * |
| thalHET |
-0.929 |
0.6316 |
-1.471 |
0.1461 |
|
| thalHOM |
-5.703 |
0.8902 |
-6.407 |
1.808e-08 |
* * * |
| age_at_collection_years_2010 |
0.1745 |
0.08895 |
1.962 |
0.05402 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 70 |
2.334 |
0.4236 |
0.3974 |
| (Intercept) |
24.25 |
27.58 |
| thalHET |
-2.19 |
0.332 |
| thalHOM |
-7.481 |
-3.926 |
| age_at_collection_years_2010 |
-0.003105 |
0.3521 |
- thal emmean SE df lower.CL upper.CL
- NORM 27.32054 0.4998671 66 26.32253 28.31856
- HET 26.39150 0.3809284 66 25.63095 27.15205
- HOM 21.61711 0.7400781 66 20.13950 23.09473

| (Intercept) |
27.23 |
0.5081 |
53.59 |
9.584e-57 |
* * * |
| thalHET |
-0.7536 |
0.6384 |
-1.18 |
0.242 |
|
| thalHOM |
-5.717 |
0.9089 |
-6.291 |
2.753e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 70 |
2.383 |
0.39 |
0.3718 |
| (Intercept) |
26.21 |
28.24 |
| thalHET |
-2.028 |
0.5207 |
| thalHOM |
-7.531 |
-3.903 |
- thal emmean SE df lower.CL upper.CL
- NORM 27.22727 0.5080667 67 26.21317 28.24138
- HET 26.47368 0.3865808 67 25.70207 27.24530
- HOM 21.51000 0.7535848 67 20.00584 23.01416

| (Intercept) |
24.73 |
0.9607 |
25.74 |
1.853e-36 |
* * * |
| sickleHET |
-1.809 |
0.9193 |
-1.967 |
0.05327 |
|
| age_at_collection_years_2010 |
0.1962 |
0.109 |
1.799 |
0.0765 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 70 |
2.898 |
0.09769 |
0.07076 |
| (Intercept) |
22.81 |
26.65 |
| sickleHET |
-3.644 |
0.02624 |
| age_at_collection_years_2010 |
-0.02147 |
0.4138 |
- sickle emmean SE df lower.CL upper.CL
- NORM 26.31151 0.3805807 67 25.55186 27.07115
- HET 24.50272 0.8368133 67 22.83244 26.17301

| (Intercept) |
26.32 |
0.3868 |
68.04 |
2.806e-64 |
* * * |
| sickleHET |
-1.842 |
0.9342 |
-1.972 |
0.05267 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 70 |
2.946 |
0.0541 |
0.04019 |
| (Intercept) |
25.55 |
27.09 |
| sickleHET |
-3.706 |
0.02183 |
- sickle emmean SE df lower.CL upper.CL
- NORM 26.31724 0.3867760 68 25.54544 27.08904
- HET 24.47500 0.8503209 68 22.77821 26.17179

| (Intercept) |
26.26 |
0.2098 |
125.2 |
0 |
* * * |
| thalHET |
-1.705 |
0.2741 |
-6.221 |
1.091e-09 |
* * * |
| thalHOM |
-5.01 |
0.3368 |
-14.88 |
2.398e-41 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
2.621 |
0.3198 |
0.3169 |
| (Intercept) |
25.85 |
26.67 |
| thalHET |
-2.243 |
-1.166 |
| thalHOM |
-5.672 |
-4.349 |
| (Intercept) |
24.06 |
0.315 |
76.39 |
7.319e-268 |
* * * |
| thalHET |
-1.83 |
0.2543 |
-7.194 |
2.484e-12 |
* * * |
| thalHOM |
-5.202 |
0.3128 |
-16.63 |
3.226e-49 |
* * * |
| age_at_collection_years_2010 |
0.303 |
0.03411 |
8.883 |
1.369e-17 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
2.428 |
0.4172 |
0.4135 |
| (Intercept) |
23.44 |
24.68 |
| thalHET |
-2.329 |
-1.33 |
| thalHOM |
-5.816 |
-4.587 |
| age_at_collection_years_2010 |
0.236 |
0.37 |
thal _ NORM ________________________________________________________________
| (Intercept) |
23.16 |
0.5782 |
40.05 |
1.155e-82 |
* * * |
| g6pd_202_rtpcrHET |
0.8628 |
0.6561 |
1.315 |
0.1905 |
|
| g6pd_202_rtpcrHOM/HEMI |
1.155 |
0.6671 |
1.731 |
0.08553 |
|
| age_at_collection_years_2010 |
0.3871 |
0.06971 |
5.552 |
1.226e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 156 |
2.858 |
0.1858 |
0.1697 |
| (Intercept) |
22.02 |
24.3 |
| g6pd_202_rtpcrHET |
-0.4334 |
2.159 |
| g6pd_202_rtpcrHOM/HEMI |
-0.1634 |
2.472 |
| age_at_collection_years_2010 |
0.2493 |
0.5248 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 25.97087 0.2712754 152 25.43491 26.50683
- HET 26.83368 0.5970949 152 25.65400 28.01336
- HOM/HEMI 27.12539 0.6095419 152 25.92112 28.32966
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
25.99 |
0.2965 |
87.67 |
1.075e-132 |
* * * |
| g6pd_202_rtpcrHET |
0.6271 |
0.7157 |
0.8763 |
0.3822 |
|
| g6pd_202_rtpcrHOM/HEMI |
1.233 |
0.729 |
1.691 |
0.0929 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 156 |
3.124 |
0.0206 |
0.007802 |
| (Intercept) |
25.41 |
26.58 |
| g6pd_202_rtpcrHET |
-0.7868 |
2.041 |
| g6pd_202_rtpcrHOM/HEMI |
-0.2076 |
2.673 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 25.99459 0.2965065 153 25.40882 26.58037
- HET 26.62174 0.6513761 153 25.33489 27.90859
- HOM/HEMI 27.22727 0.6660156 153 25.91150 28.54305
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
23.57 |
0.5706 |
41.31 |
7.104e-85 |
* * * |
| sickleHET |
-0.5405 |
0.6187 |
-0.8735 |
0.3838 |
|
| age_at_collection_years_2010 |
0.383 |
0.07007 |
5.466 |
1.83e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 156 |
2.879 |
0.1681 |
0.1572 |
| (Intercept) |
22.44 |
24.7 |
| sickleHET |
-1.763 |
0.6819 |
| age_at_collection_years_2010 |
0.2446 |
0.5215 |
- sickle emmean SE df lower.CL upper.CL
- NORM 26.35098 0.2525323 153 25.85208 26.84988
- HET 25.81051 0.5648023 153 24.69469 26.92632

| (Intercept) |
26.37 |
0.2752 |
95.81 |
3.59e-139 |
* * * |
| sickleHET |
-0.6269 |
0.674 |
-0.9301 |
0.3538 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 156 |
3.138 |
0.005586 |
-0.0008713 |
| (Intercept) |
25.82 |
26.91 |
| sickleHET |
-1.958 |
0.7046 |
- sickle emmean SE df lower.CL upper.CL
- NORM 26.36538 0.2751782 154 25.82177 26.90900
- HET 25.73846 0.6153173 154 24.52291 26.95401

thal _ HET ________________________________________________________________
| (Intercept) |
21.82 |
0.3799 |
57.43 |
3.266e-133 |
* * * |
| g6pd_202_rtpcrHET |
0.9952 |
0.3889 |
2.559 |
0.01116 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.241 |
0.3984 |
5.624 |
5.708e-08 |
* * * |
| age_at_collection_years_2010 |
0.2829 |
0.04394 |
6.439 |
7.626e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 221 |
2.171 |
0.2749 |
0.2649 |
| (Intercept) |
21.07 |
22.57 |
| g6pd_202_rtpcrHET |
0.2288 |
1.762 |
| g6pd_202_rtpcrHOM/HEMI |
1.455 |
3.026 |
| age_at_collection_years_2010 |
0.1963 |
0.3695 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 23.99070 0.1816040 217 23.63277 24.34864
- HET 24.98594 0.3440784 217 24.30778 25.66410
- HOM/HEMI 26.23136 0.3542285 217 25.53319 26.92953
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
23.97 |
0.1977 |
121.2 |
5.025e-202 |
* * * |
| g6pd_202_rtpcrHET |
0.8697 |
0.4229 |
2.057 |
0.04092 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.506 |
0.4315 |
5.808 |
2.223e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 221 |
2.364 |
0.1364 |
0.1284 |
| (Intercept) |
23.58 |
24.36 |
| g6pd_202_rtpcrHET |
0.03623 |
1.703 |
| g6pd_202_rtpcrHOM/HEMI |
1.655 |
3.356 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 23.96783 0.1977012 218 23.57818 24.35748
- HET 24.83750 0.3738069 218 24.10076 25.57424
- HOM/HEMI 26.47368 0.3835178 218 25.71781 27.22956
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
22.32 |
0.3893 |
57.33 |
1.895e-133 |
* * * |
| sickleHET |
-0.9446 |
0.4253 |
-2.221 |
0.02739 |
* |
| age_at_collection_years_2010 |
0.3114 |
0.04634 |
6.72 |
1.561e-10 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 221 |
2.302 |
0.181 |
0.1735 |
| (Intercept) |
21.55 |
23.08 |
| sickleHET |
-1.783 |
-0.1063 |
| age_at_collection_years_2010 |
0.2201 |
0.4027 |
- sickle emmean SE df lower.CL upper.CL
- NORM 24.70571 0.1688848 218 24.37285 25.03856
- HET 23.76111 0.3900404 218 22.99237 24.52984

| (Intercept) |
24.67 |
0.1851 |
133.3 |
1.182e-211 |
* * * |
| sickleHET |
-0.7354 |
0.465 |
-1.582 |
0.1152 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 221 |
2.524 |
0.01129 |
0.006778 |
| (Intercept) |
24.31 |
25.04 |
| sickleHET |
-1.652 |
0.181 |
- sickle emmean SE df lower.CL upper.CL
- NORM 24.67258 0.1850525 219 24.30787 25.03729
- HET 23.93714 0.4265965 219 23.09638 24.77790

thal _ HOM ________________________________________________________________
| (Intercept) |
20.02 |
0.4989 |
40.13 |
2.259e-61 |
* * * |
| g6pd_202_rtpcrHET |
0.6304 |
0.5455 |
1.156 |
0.2507 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.4435 |
0.5864 |
0.7564 |
0.4513 |
|
| age_at_collection_years_2010 |
0.1403 |
0.05876 |
2.387 |
0.01895 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 99 |
1.744 |
0.07856 |
0.04946 |
| (Intercept) |
19.03 |
21.01 |
| g6pd_202_rtpcrHET |
-0.4525 |
1.713 |
| g6pd_202_rtpcrHOM/HEMI |
-0.7206 |
1.608 |
| age_at_collection_years_2010 |
0.02363 |
0.2569 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 21.12929 0.1987981 95 20.73463 21.52396
- HET 21.75968 0.5071632 95 20.75283 22.76653
- HOM/HEMI 21.57283 0.5519944 95 20.47698 22.66868
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
21.11 |
0.2035 |
103.8 |
2.184e-100 |
* * * |
| g6pd_202_rtpcrHET |
0.794 |
0.5542 |
1.433 |
0.1552 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.3957 |
0.6003 |
0.6592 |
0.5113 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 99 |
1.786 |
0.02327 |
0.002927 |
| (Intercept) |
20.71 |
21.52 |
| g6pd_202_rtpcrHET |
-0.3061 |
1.894 |
| g6pd_202_rtpcrHOM/HEMI |
-0.7958 |
1.587 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 21.11429 0.2035044 96 20.71033 21.51824
- HET 21.90833 0.5154997 96 20.88507 22.93159
- HOM/HEMI 21.51000 0.5647017 96 20.38908 22.63092
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
19.81 |
0.503 |
39.39 |
4.638e-61 |
* * * |
| sickleHET |
0.9305 |
0.4607 |
2.02 |
0.04618 |
* |
| age_at_collection_years_2010 |
0.1621 |
0.05772 |
2.809 |
0.00603 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 99 |
1.714 |
0.1001 |
0.08134 |
| (Intercept) |
18.81 |
20.81 |
| sickleHET |
0.01609 |
1.845 |
| age_at_collection_years_2010 |
0.04754 |
0.2767 |
- sickle emmean SE df lower.CL upper.CL
- NORM 21.09072 0.1895671 96 20.71443 21.46701
- HET 22.02124 0.4186592 96 21.19021 22.85228

| (Intercept) |
21.12 |
0.1959 |
107.8 |
8.404e-103 |
* * * |
| sickleHET |
0.7628 |
0.4727 |
1.614 |
0.1098 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 99 |
1.774 |
0.02614 |
0.0161 |
| (Intercept) |
20.73 |
21.51 |
| sickleHET |
-0.1754 |
1.701 |
- sickle emmean SE df lower.CL upper.CL
- NORM 21.11951 0.195895 97 20.73071 21.50831
- HET 21.88235 0.430235 97 21.02846 22.73625

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
| (Intercept) |
24.49 |
0.1589 |
154.1 |
0 |
* * * |
| sickleHET |
-0.4037 |
0.3926 |
-1.028 |
0.3043 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
3.171 |
0.002226 |
0.0001207 |
| (Intercept) |
24.18 |
24.81 |
| sickleHET |
-1.175 |
0.3678 |
| (Intercept) |
22.49 |
0.3587 |
62.68 |
2.899e-231 |
* * * |
| sickleHET |
-0.405 |
0.378 |
-1.071 |
0.2845 |
|
| age_at_collection_years_2010 |
0.2647 |
0.04277 |
6.19 |
1.308e-09 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
3.053 |
0.07699 |
0.07309 |
| (Intercept) |
21.78 |
23.19 |
| sickleHET |
-1.148 |
0.3378 |
| age_at_collection_years_2010 |
0.1807 |
0.3488 |
sickle _ NORM ________________________________________________________________
| (Intercept) |
22.17 |
0.3874 |
57.24 |
5.065e-193 |
* * * |
| g6pd_202_rtpcrHET |
1.017 |
0.4244 |
2.396 |
0.01703 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.173 |
0.4341 |
5.006 |
8.401e-07 |
* * * |
| age_at_collection_years_2010 |
0.2437 |
0.04583 |
5.318 |
1.764e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 398 |
3.002 |
0.1308 |
0.1242 |
| (Intercept) |
21.41 |
22.93 |
| g6pd_202_rtpcrHET |
0.1826 |
1.851 |
| g6pd_202_rtpcrHOM/HEMI |
1.32 |
3.027 |
| age_at_collection_years_2010 |
0.1536 |
0.3339 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 24.02091 0.1797959 394 23.66743 24.37439
- HET 25.03781 0.3843979 394 24.28208 25.79353
- HOM/HEMI 26.19415 0.3948908 394 25.41779 26.97051
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
24 |
0.1858 |
129.1 |
0 |
* * * |
| g6pd_202_rtpcrHET |
1.034 |
0.4388 |
2.358 |
0.01888 |
* |
| g6pd_202_rtpcrHOM/HEMI |
2.32 |
0.448 |
5.18 |
3.544e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 398 |
3.104 |
0.06838 |
0.06367 |
| (Intercept) |
23.63 |
24.36 |
| g6pd_202_rtpcrHET |
0.1718 |
1.897 |
| g6pd_202_rtpcrHOM/HEMI |
1.44 |
3.201 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 23.99677 0.1858416 395 23.63141 24.36214
- HET 25.03115 0.3974478 395 24.24977 25.81253
- HOM/HEMI 26.31724 0.4075970 395 25.51591 27.11857
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
24.12 |
0.3419 |
70.54 |
1.359e-225 |
* * * |
| thalHET |
-1.775 |
0.2755 |
-6.444 |
3.401e-10 |
* * * |
| thalHOM |
-5.483 |
0.3408 |
-16.09 |
3.976e-45 |
* * * |
| age_at_collection_years_2010 |
0.3078 |
0.03682 |
8.358 |
1.108e-15 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 398 |
2.408 |
0.4406 |
0.4364 |
| (Intercept) |
23.45 |
24.79 |
| thalHET |
-2.317 |
-1.234 |
| thalHOM |
-6.153 |
-4.813 |
| age_at_collection_years_2010 |
0.2354 |
0.3802 |
- thal emmean SE df lower.CL upper.CL
- NORM 26.45290 0.2114921 394 26.03710 26.86869
- HET 24.67755 0.1765952 394 24.33037 25.02474
- HOM 20.96950 0.2665709 394 20.44542 21.49358

| (Intercept) |
26.37 |
0.2289 |
115.2 |
4.741e-306 |
* * * |
| thalHET |
-1.693 |
0.2984 |
-5.674 |
2.706e-08 |
* * * |
| thalHOM |
-5.246 |
0.3681 |
-14.25 |
1.756e-37 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 398 |
2.61 |
0.3414 |
0.3381 |
| (Intercept) |
25.92 |
26.82 |
| thalHET |
-2.279 |
-1.106 |
| thalHOM |
-5.969 |
-4.522 |
- thal emmean SE df lower.CL upper.CL
- NORM 26.36538 0.2289042 395 25.91536 26.81541
- HET 24.67258 0.1913679 395 24.29635 25.04881
- HOM 21.11951 0.2882163 395 20.55288 21.68614

sickle _ HET ________________________________________________________________
| (Intercept) |
21.36 |
0.9046 |
23.61 |
3.451e-36 |
* * * |
| g6pd_202_rtpcrHET |
1.044 |
0.8718 |
1.197 |
0.235 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.5507 |
0.9088 |
0.606 |
0.5464 |
|
| age_at_collection_years_2010 |
0.3237 |
0.1033 |
3.134 |
0.002473 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 78 |
2.838 |
0.1235 |
0.08796 |
| (Intercept) |
19.56 |
23.16 |
| g6pd_202_rtpcrHET |
-0.6932 |
2.781 |
| g6pd_202_rtpcrHOM/HEMI |
-1.26 |
2.362 |
| age_at_collection_years_2010 |
0.1179 |
0.5295 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 23.81765 0.3947058 74 23.03119 24.60412
- HET 24.86155 0.7717227 74 23.32386 26.39924
- HOM/HEMI 24.36835 0.8198839 74 22.73470 26.00201
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
23.91 |
0.416 |
57.48 |
8.959e-64 |
* * * |
| g6pd_202_rtpcrHET |
0.5008 |
0.9033 |
0.5545 |
0.5809 |
|
| g6pd_202_rtpcrHOM/HEMI |
0.5615 |
0.9608 |
0.5845 |
0.5607 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 78 |
3 |
0.007166 |
-0.01931 |
| (Intercept) |
23.08 |
24.74 |
| g6pd_202_rtpcrHET |
-1.299 |
2.3 |
| g6pd_202_rtpcrHOM/HEMI |
-1.352 |
2.475 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 23.91346 0.4160191 75 23.08471 24.74221
- HET 24.41429 0.8017720 75 22.81707 26.01150
- HOM/HEMI 24.47500 0.8660127 75 22.74981 26.20019
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
23.49 |
0.7952 |
29.54 |
1.063e-42 |
* * * |
| thalHET |
-2.172 |
0.646 |
-3.362 |
0.001228 |
* * |
| thalHOM |
-3.844 |
0.7681 |
-5.005 |
3.658e-06 |
* * * |
| age_at_collection_years_2010 |
0.3178 |
0.08928 |
3.559 |
0.000654 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 78 |
2.462 |
0.34 |
0.3132 |
| (Intercept) |
21.91 |
25.07 |
| thalHET |
-3.459 |
-0.8846 |
| thalHOM |
-5.375 |
-2.314 |
| age_at_collection_years_2010 |
0.1399 |
0.4957 |
- thal emmean SE df lower.CL upper.CL
- NORM 25.90211 0.4851118 74 24.93550 26.86872
- HET 23.73039 0.4202653 74 22.89300 24.56779
- HOM 22.05773 0.5992631 74 20.86367 23.25178

| (Intercept) |
25.74 |
0.5191 |
49.58 |
4.436e-59 |
* * * |
| thalHET |
-1.801 |
0.6853 |
-2.628 |
0.0104 |
* |
| thalHOM |
-3.856 |
0.8256 |
-4.67 |
1.292e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 78 |
2.647 |
0.227 |
0.2064 |
| (Intercept) |
24.7 |
26.77 |
| thalHET |
-3.167 |
-0.436 |
| thalHOM |
-5.501 |
-2.211 |
- thal emmean SE df lower.CL upper.CL
- NORM 25.73846 0.5191354 75 24.70429 26.77263
- HET 23.93714 0.4474384 75 23.04580 24.82849
- HOM 21.88235 0.6420115 75 20.60340 23.16131

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
mchc_2010
####All_vs_g6pd+thal________________________________________________________________
| (Intercept) |
33.66 |
0.08381 |
401.6 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1706 |
0.1263 |
-1.351 |
0.1775 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2327 |
0.1301 |
-1.789 |
0.07428 |
|
| thalHET |
-0.4353 |
0.1032 |
-4.22 |
2.93e-05 |
* * * |
| thalHOM |
-1.261 |
0.1267 |
-9.951 |
2.653e-21 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
| 476 |
0.9846 |
0.1774 |
0.1704 |
| (Intercept) |
33.5 |
33.83 |
| g6pd_202_rtpcrHET |
-0.4189 |
0.07761 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4882 |
0.02291 |
| thalHET |
-0.6381 |
-0.2326 |
| thalHOM |
-1.51 |
-1.012 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 33.66053 0.0838134 471 33.49584 33.82522
- HET NORM 33.48991 0.1319772 471 33.23057 33.74925
- HOM/HEMI NORM 33.42787 0.1351701 471 33.16226 33.69348
- NORM HET 33.22518 0.0748291 471 33.07814 33.37222
- HET HET 33.05457 0.1213800 471 32.81605 33.29308
- HOM/HEMI HET 32.99253 0.1248801 471 32.74714 33.23792
- NORM HOM 32.39974 0.1013630 471 32.20056 32.59892
- HET HOM 32.22912 0.1474741 471 31.93933 32.51891
- HOM/HEMI HOM 32.16708 0.1517707 471 31.86885 32.46531
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

| (Intercept) |
33.12 |
0.1266 |
261.6 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1528 |
0.1225 |
-1.248 |
0.2128 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2721 |
0.1263 |
-2.155 |
0.03169 |
* |
| thalHET |
-0.4657 |
0.1001 |
-4.65 |
4.324e-06 |
* * * |
| thalHOM |
-1.309 |
0.1231 |
-10.63 |
8.197e-24 |
* * * |
| age_at_collection_years_2010 |
0.07508 |
0.01344 |
5.587 |
3.913e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
| 476 |
0.9545 |
0.2286 |
0.2204 |
| (Intercept) |
32.87 |
33.37 |
| g6pd_202_rtpcrHET |
-0.3935 |
0.08789 |
| g6pd_202_rtpcrHOM/HEMI |
-0.5202 |
-0.02395 |
| thalHET |
-0.6625 |
-0.2689 |
| thalHOM |
-1.551 |
-1.067 |
| age_at_collection_years_2010 |
0.04867 |
0.1015 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 33.68770 0.0813931 470 33.52776 33.84764
- HET NORM 33.53488 0.1281900 470 33.28298 33.78677
- HOM/HEMI NORM 33.41561 0.1310506 470 33.15809 33.67313
- NORM HET 33.22203 0.0725406 470 33.07949 33.36457
- HET HET 33.06921 0.1176935 470 32.83794 33.30048
- HOM/HEMI HET 32.94994 0.1212970 470 32.71159 33.18829
- NORM HOM 32.37836 0.0983345 470 32.18513 32.57159
- HET HOM 32.22554 0.1429610 470 31.94462 32.50646
- HOM/HEMI HOM 32.10627 0.1475267 470 31.81638 32.39617
| NORM |
NORM |
111 |
| NORM |
HET |
143 |
| NORM |
HOM |
77 |
| HET |
NORM |
23 |
| HET |
HET |
40 |
| HET |
HOM |
12 |
| HOM/HEMI |
NORM |
22 |
| HOM/HEMI |
HET |
38 |
| HOM/HEMI |
HOM |
10 |

####All_vs_g6pd+sickle________________________________________________________________
| (Intercept) |
33.22 |
0.0629 |
528.1 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1161 |
0.138 |
-0.8416 |
0.4004 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1643 |
0.1419 |
-1.158 |
0.2475 |
|
| sickleHET |
-0.2369 |
0.1336 |
-1.773 |
0.07691 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
| 476 |
1.079 |
0.01052 |
0.004231 |
| (Intercept) |
33.09 |
33.34 |
| g6pd_202_rtpcrHET |
-0.3873 |
0.155 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4432 |
0.1146 |
| sickleHET |
-0.4995 |
0.02569 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 33.21637 0.0629004 472 33.09277 33.33997
- HET NORM 33.10022 0.1270363 472 32.85060 33.34985
- HOM/HEMI NORM 33.05204 0.1309544 472 32.79472 33.30937
- NORM HET 32.97946 0.1272955 472 32.72933 33.22960
- HET HET 32.86331 0.1653174 472 32.53846 33.18816
- HOM/HEMI HET 32.81513 0.1699557 472 32.48117 33.14910
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
32.72 |
0.128 |
255.6 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1003 |
0.1355 |
-0.7404 |
0.4594 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1977 |
0.1395 |
-1.417 |
0.157 |
|
| sickleHET |
-0.2374 |
0.1311 |
-1.81 |
0.0709 |
|
| age_at_collection_years_2010 |
0.06516 |
0.01486 |
4.385 |
1.435e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
| 476 |
1.059 |
0.04932 |
0.04125 |
| (Intercept) |
32.47 |
32.98 |
| g6pd_202_rtpcrHET |
-0.3665 |
0.1659 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4717 |
0.07638 |
| sickleHET |
-0.495 |
0.0203 |
| age_at_collection_years_2010 |
0.03596 |
0.09436 |
- g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
- NORM NORM 33.21886 0.0617228 471 33.09757 33.34014
- HET NORM 33.11856 0.1247228 471 32.87348 33.36364
- HOM/HEMI NORM 33.02117 0.1286900 471 32.76830 33.27405
- NORM HET 32.98148 0.1249078 471 32.73603 33.22693
- HET HET 32.88118 0.1622666 471 32.56233 33.20004
- HOM/HEMI HET 32.78380 0.1669198 471 32.45580 33.11180
| NORM |
NORM |
279 |
| NORM |
HET |
52 |
| HET |
NORM |
61 |
| HET |
HET |
14 |
| HOM/HEMI |
NORM |
58 |
| HOM/HEMI |
HET |
12 |

| (Intercept) |
33.18 |
0.05943 |
558.3 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1232 |
0.1383 |
-0.8907 |
0.3735 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1677 |
0.1422 |
-1.179 |
0.2389 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
1.081 |
0.003931 |
-0.0002804 |
| (Intercept) |
33.06 |
33.3 |
| g6pd_202_rtpcrHET |
-0.3948 |
0.1485 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4472 |
0.1118 |
| (Intercept) |
32.69 |
0.1267 |
258 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.1073 |
0.1357 |
-0.7906 |
0.4296 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2011 |
0.1398 |
-1.438 |
0.151 |
|
| age_at_collection_years_2010 |
0.06514 |
0.0149 |
4.373 |
1.512e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
1.061 |
0.04271 |
0.03662 |
| (Intercept) |
32.44 |
32.94 |
| g6pd_202_rtpcrHET |
-0.3741 |
0.1594 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4758 |
0.07363 |
| age_at_collection_years_2010 |
0.03586 |
0.09441 |
g6pd_202_rtpcr _ NORM ________________________________________________________________
| (Intercept) |
33 |
0.1509 |
218.7 |
0 |
* * * |
| thalHET |
-0.454 |
0.123 |
-3.692 |
0.0002604 |
* * * |
| thalHOM |
-1.181 |
0.1443 |
-8.187 |
6.074e-15 |
* * * |
| age_at_collection_years_2010 |
0.08667 |
0.0163 |
5.317 |
1.955e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 331 |
0.9715 |
0.2179 |
0.2107 |
| (Intercept) |
32.7 |
33.29 |
| thalHET |
-0.696 |
-0.2121 |
| thalHOM |
-1.465 |
-0.8974 |
| age_at_collection_years_2010 |
0.05461 |
0.1187 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.65009 0.0922826 327 33.46854 33.83163
- HET 33.19605 0.0812437 327 33.03623 33.35588
- HOM 32.46889 0.1107804 327 32.25096 32.68683

| (Intercept) |
33.63 |
0.09597 |
350.4 |
0 |
* * * |
| thalHET |
-0.4306 |
0.1279 |
-3.367 |
0.0008505 |
* * * |
| thalHOM |
-1.141 |
0.15 |
-7.609 |
2.951e-13 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 331 |
1.011 |
0.1503 |
0.1451 |
| (Intercept) |
33.44 |
33.82 |
| thalHET |
-0.6822 |
-0.179 |
| thalHOM |
-1.436 |
-0.846 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.63063 0.0959673 328 33.44184 33.81942
- HET 33.20000 0.0845506 328 33.03367 33.36633
- HOM 32.48961 0.1152230 328 32.26294 32.71628

| (Intercept) |
32.6 |
0.1478 |
220.5 |
0 |
* * * |
| sickleHET |
-0.1786 |
0.1607 |
-1.112 |
0.2672 |
|
| age_at_collection_years_2010 |
0.08062 |
0.01782 |
4.523 |
8.54e-06 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 331 |
1.063 |
0.06094 |
0.05522 |
| (Intercept) |
32.31 |
32.89 |
| sickleHET |
-0.4948 |
0.1375 |
| age_at_collection_years_2010 |
0.04555 |
0.1157 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.20722 0.0636438 328 33.08201 33.33242
- HET 33.02859 0.1475196 328 32.73839 33.31880

| (Intercept) |
33.2 |
0.06549 |
507 |
0 |
* * * |
| sickleHET |
-0.1464 |
0.1652 |
-0.886 |
0.3763 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 331 |
1.094 |
0.00238 |
-0.0006522 |
| (Intercept) |
33.07 |
33.33 |
| sickleHET |
-0.4714 |
0.1786 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.20215 0.0654884 329 33.07332 33.33098
- HET 33.05577 0.1516928 329 32.75736 33.35418

g6pd_202_rtpcr _ HET ________________________________________________________________
| (Intercept) |
33.29 |
0.307 |
108.4 |
1.275e-80 |
* * * |
| thalHET |
-0.6954 |
0.2512 |
-2.769 |
0.007176 |
* * |
| thalHOM |
-1.638 |
0.3499 |
-4.68 |
1.335e-05 |
* * * |
| age_at_collection_years_2010 |
0.05481 |
0.03471 |
1.579 |
0.1187 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 75 |
0.958 |
0.2421 |
0.2101 |
| (Intercept) |
32.68 |
33.9 |
| thalHET |
-1.196 |
-0.1946 |
| thalHOM |
-2.335 |
-0.9399 |
| age_at_collection_years_2010 |
-0.01439 |
0.124 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.68888 0.2008136 71 33.28846 34.08929
- HET 32.99351 0.1515758 71 32.69127 33.29574
- HOM 32.05129 0.2824211 71 31.48816 32.61442

| (Intercept) |
33.66 |
0.2018 |
166.8 |
6.391e-95 |
* * * |
| thalHET |
-0.6715 |
0.2533 |
-2.651 |
0.009858 |
* * |
| thalHOM |
-1.515 |
0.3447 |
-4.395 |
3.749e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 75 |
0.9679 |
0.2155 |
0.1937 |
| (Intercept) |
33.25 |
34.06 |
| thalHET |
-1.176 |
-0.1666 |
| thalHOM |
-2.202 |
-0.8277 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.65652 0.2018284 72 33.25418 34.05886
- HET 32.98500 0.1530440 72 32.67991 33.29009
- HOM 32.14167 0.2794188 72 31.58466 32.69868

| (Intercept) |
33.02 |
0.3236 |
102 |
1.257e-79 |
* * * |
| sickleHET |
-0.3406 |
0.325 |
-1.048 |
0.2981 |
|
| age_at_collection_years_2010 |
0.01335 |
0.03869 |
0.3451 |
0.731 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 75 |
1.083 |
0.01866 |
-0.0086 |
| (Intercept) |
32.38 |
33.67 |
| sickleHET |
-0.9886 |
0.3073 |
| age_at_collection_years_2010 |
-0.06378 |
0.09048 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.11959 0.1389507 72 32.84259 33.39658
- HET 32.77894 0.2924287 72 32.19600 33.36189

| (Intercept) |
33.12 |
0.1378 |
240.4 |
1.41e-107 |
* * * |
| sickleHET |
-0.3587 |
0.3189 |
-1.125 |
0.2644 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 75 |
1.076 |
0.01704 |
0.00357 |
| (Intercept) |
32.85 |
33.4 |
| sickleHET |
-0.9942 |
0.2769 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.12295 0.1377696 73 32.84838 33.39753
- HET 32.76429 0.2875771 73 32.19115 33.33743

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________
| (Intercept) |
33.15 |
0.3009 |
110.1 |
1.548e-76 |
* * * |
| thalHET |
-0.2784 |
0.2279 |
-1.222 |
0.2261 |
|
| thalHOM |
-1.822 |
0.3211 |
-5.673 |
3.372e-07 |
* * * |
| age_at_collection_years_2010 |
0.03424 |
0.03209 |
1.067 |
0.2899 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 70 |
0.842 |
0.3531 |
0.3237 |
| (Intercept) |
32.55 |
33.75 |
| thalHET |
-0.7334 |
0.1765 |
| thalHOM |
-2.463 |
-1.181 |
| age_at_collection_years_2010 |
-0.02983 |
0.09831 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.42285 0.1803283 66 33.06281 33.78288
- HET 33.14440 0.1374208 66 32.87003 33.41877
- HOM 31.60102 0.2669850 66 31.06796 32.13407

| (Intercept) |
33.4 |
0.1797 |
185.9 |
1.236e-92 |
* * * |
| thalHET |
-0.244 |
0.2258 |
-1.081 |
0.2837 |
|
| thalHOM |
-1.825 |
0.3214 |
-5.676 |
3.215e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 70 |
0.8428 |
0.342 |
0.3223 |
| (Intercept) |
33.05 |
33.76 |
| thalHET |
-0.6947 |
0.2067 |
| thalHOM |
-2.466 |
-1.183 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.40455 0.1796958 67 33.04587 33.76322
- HET 33.16053 0.1367280 67 32.88762 33.43344
- HOM 31.58000 0.2665319 67 31.04800 32.11200

| (Intercept) |
32.76 |
0.3355 |
97.63 |
5.718e-74 |
* * * |
| sickleHET |
-0.4894 |
0.3211 |
-1.524 |
0.1321 |
|
| age_at_collection_years_2010 |
0.04183 |
0.03808 |
1.099 |
0.2759 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 70 |
1.012 |
0.05099 |
0.02266 |
| (Intercept) |
32.09 |
33.43 |
| sickleHET |
-1.13 |
0.1514 |
| age_at_collection_years_2010 |
-0.03417 |
0.1178 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.09533 0.1329110 67 32.83004 33.36062
- HET 32.60591 0.2922421 67 32.02260 33.18923

| (Intercept) |
33.1 |
0.1331 |
248.6 |
2.367e-102 |
* * * |
| sickleHET |
-0.4966 |
0.3215 |
-1.545 |
0.1271 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 70 |
1.014 |
0.03389 |
0.01969 |
| (Intercept) |
32.83 |
33.36 |
| sickleHET |
-1.138 |
0.145 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.09655 0.1331085 68 32.83094 33.36217
- HET 32.60000 0.2926369 68 32.01605 33.18395

| (Intercept) |
33.6 |
0.07902 |
425.2 |
0 |
* * * |
| thalHET |
-0.4483 |
0.1032 |
-4.343 |
1.719e-05 |
* * * |
| thalHOM |
-1.247 |
0.1268 |
-9.833 |
6.968e-21 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
0.987 |
0.1699 |
0.1664 |
| (Intercept) |
33.45 |
33.76 |
| thalHET |
-0.6511 |
-0.2455 |
| thalHOM |
-1.496 |
-0.9978 |
| (Intercept) |
33.07 |
0.1242 |
266.1 |
0 |
* * * |
| thalHET |
-0.4787 |
0.1003 |
-4.772 |
2.438e-06 |
* * * |
| thalHOM |
-1.294 |
0.1234 |
-10.49 |
2.893e-23 |
* * * |
| age_at_collection_years_2010 |
0.07389 |
0.01346 |
5.491 |
6.526e-08 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
0.9579 |
0.2198 |
0.2148 |
| (Intercept) |
32.82 |
33.31 |
| thalHET |
-0.6759 |
-0.2816 |
| thalHOM |
-1.536 |
-1.051 |
| age_at_collection_years_2010 |
0.04745 |
0.1003 |
thal _ NORM ________________________________________________________________
| (Intercept) |
33.01 |
0.1952 |
169.1 |
6.134e-175 |
* * * |
| g6pd_202_rtpcrHET |
0.07775 |
0.2215 |
0.351 |
0.7261 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2433 |
0.2252 |
-1.08 |
0.2817 |
|
| age_at_collection_years_2010 |
0.08518 |
0.02354 |
3.619 |
0.0004017 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 156 |
0.9648 |
0.08539 |
0.06734 |
| (Intercept) |
32.62 |
33.39 |
| g6pd_202_rtpcrHET |
-0.3599 |
0.5154 |
| g6pd_202_rtpcrHOM/HEMI |
-0.6882 |
0.2017 |
| age_at_collection_years_2010 |
0.03868 |
0.1317 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.62541 0.0915865 152 33.44446 33.80636
- HET 33.70316 0.2015878 152 33.30489 34.10144
- HOM/HEMI 33.38213 0.2057901 152 32.97555 33.78870
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
33.63 |
0.09513 |
353.5 |
9.716e-225 |
* * * |
| g6pd_202_rtpcrHET |
0.02589 |
0.2296 |
0.1128 |
0.9104 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.2261 |
0.2339 |
-0.9666 |
0.3353 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 156 |
1.002 |
0.006574 |
-0.006412 |
| (Intercept) |
33.44 |
33.82 |
| g6pd_202_rtpcrHET |
-0.4277 |
0.4795 |
| g6pd_202_rtpcrHOM/HEMI |
-0.6882 |
0.236 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.63063 0.0951271 153 33.44270 33.81856
- HET 33.65652 0.2089786 153 33.24367 34.06938
- HOM/HEMI 33.40455 0.2136753 153 32.98241 33.82668
| NORM |
111 |
| HET |
23 |
| HOM/HEMI |
22 |

| (Intercept) |
33.05 |
0.1904 |
173.6 |
1.346e-177 |
* * * |
| sickleHET |
-0.2751 |
0.2065 |
-1.333 |
0.1847 |
|
| age_at_collection_years_2010 |
0.08297 |
0.02338 |
3.549 |
0.0005146 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 156 |
0.9607 |
0.08722 |
0.07529 |
| (Intercept) |
32.67 |
33.42 |
| sickleHET |
-0.683 |
0.1328 |
| age_at_collection_years_2010 |
0.03678 |
0.1292 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.64842 0.0842622 153 33.48195 33.81488
- HET 33.37330 0.1884572 153 33.00098 33.74561

| (Intercept) |
33.65 |
0.08737 |
385.2 |
1.057e-231 |
* * * |
| sickleHET |
-0.2938 |
0.214 |
-1.373 |
0.1717 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 156 |
0.9962 |
0.01209 |
0.005678 |
| (Intercept) |
33.48 |
33.82 |
| sickleHET |
-0.7166 |
0.1289 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.65154 0.0873715 154 33.47894 33.82414
- HET 33.35769 0.1953685 154 32.97174 33.74364

thal _ HET ________________________________________________________________
| (Intercept) |
32.65 |
0.1736 |
188 |
2.789e-242 |
* * * |
| g6pd_202_rtpcrHET |
-0.1826 |
0.1777 |
-1.027 |
0.3054 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1079 |
0.1821 |
-0.5929 |
0.5539 |
|
| age_at_collection_years_2010 |
0.07305 |
0.02008 |
3.638 |
0.0003433 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 221 |
0.9923 |
0.06346 |
0.05051 |
| (Intercept) |
32.3 |
32.99 |
| g6pd_202_rtpcrHET |
-0.5328 |
0.1677 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4668 |
0.2509 |
| age_at_collection_years_2010 |
0.03347 |
0.1126 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.20591 0.0829923 217 33.04233 33.36948
- HET 33.02333 0.1572424 217 32.71341 33.33325
- HOM/HEMI 33.09796 0.1618810 217 32.77890 33.41702
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
33.2 |
0.08527 |
389.3 |
7.331e-312 |
* * * |
| g6pd_202_rtpcrHET |
-0.215 |
0.1824 |
-1.179 |
0.2398 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.03947 |
0.1861 |
-0.2121 |
0.8322 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 221 |
1.02 |
0.006341 |
-0.002775 |
| (Intercept) |
33.03 |
33.37 |
| g6pd_202_rtpcrHET |
-0.5745 |
0.1445 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4063 |
0.3273 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.20000 0.0852731 218 33.03193 33.36807
- HET 32.98500 0.1612316 218 32.66723 33.30277
- HOM/HEMI 33.16053 0.1654201 218 32.83450 33.48655
| NORM |
143 |
| HET |
40 |
| HOM/HEMI |
38 |

| (Intercept) |
32.63 |
0.1663 |
196.2 |
3.697e-247 |
* * * |
| sickleHET |
-0.3634 |
0.1817 |
-2 |
0.04677 |
* |
| age_at_collection_years_2010 |
0.07607 |
0.0198 |
3.842 |
0.0001599 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 221 |
0.9837 |
0.07522 |
0.06674 |
| (Intercept) |
32.3 |
32.96 |
| sickleHET |
-0.7216 |
-0.005238 |
| age_at_collection_years_2010 |
0.03705 |
0.1151 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.21186 0.0721621 218 33.06963 33.35408
- HET 32.84842 0.1666588 218 32.51995 33.17689

| (Intercept) |
33.2 |
0.07436 |
446.5 |
0 |
* * * |
| sickleHET |
-0.3123 |
0.1869 |
-1.671 |
0.09606 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 221 |
1.014 |
0.0126 |
0.008088 |
| (Intercept) |
33.06 |
33.35 |
| sickleHET |
-0.6806 |
0.05594 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.20376 0.0743633 219 33.05720 33.35032
- HET 32.89143 0.1714277 219 32.55357 33.22929

thal _ HOM ________________________________________________________________
| (Intercept) |
32.03 |
0.2402 |
133.3 |
7.496e-110 |
* * * |
| g6pd_202_rtpcrHET |
-0.4165 |
0.2626 |
-1.586 |
0.1161 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.8896 |
0.2824 |
-3.15 |
0.00218 |
* * |
| age_at_collection_years_2010 |
0.05872 |
0.02829 |
2.076 |
0.04064 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 99 |
0.8396 |
0.1409 |
0.1138 |
| (Intercept) |
31.56 |
32.51 |
| g6pd_202_rtpcrHET |
-0.9379 |
0.105 |
| g6pd_202_rtpcrHOM/HEMI |
-1.45 |
-0.329 |
| age_at_collection_years_2010 |
0.002555 |
0.1149 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 32.49589 0.0957237 95 32.30586 32.68593
- HET 32.07944 0.2442051 95 31.59463 32.56425
- HOM/HEMI 31.60630 0.2657918 95 31.07864 32.13397
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
32.49 |
0.09731 |
333.9 |
5.979e-149 |
* * * |
| g6pd_202_rtpcrHET |
-0.3479 |
0.265 |
-1.313 |
0.1923 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.9096 |
0.287 |
-3.169 |
0.002052 |
* * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 99 |
0.8539 |
0.102 |
0.08326 |
| (Intercept) |
32.3 |
32.68 |
| g6pd_202_rtpcrHET |
-0.874 |
0.1781 |
| g6pd_202_rtpcrHOM/HEMI |
-1.479 |
-0.3399 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 32.48961 0.0973102 96 32.29645 32.68277
- HET 32.14167 0.2464978 96 31.65237 32.63096
- HOM/HEMI 31.58000 0.2700248 96 31.04401 32.11600
| NORM |
77 |
| HET |
12 |
| HOM/HEMI |
10 |

| (Intercept) |
31.88 |
0.2592 |
123 |
2.011e-107 |
* * * |
| sickleHET |
0.0787 |
0.2374 |
0.3315 |
0.741 |
|
| age_at_collection_years_2010 |
0.05854 |
0.02975 |
1.968 |
0.05198 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 99 |
0.8834 |
0.03883 |
0.0188 |
| (Intercept) |
31.37 |
32.39 |
| sickleHET |
-0.3926 |
0.55 |
| age_at_collection_years_2010 |
-0.0005115 |
0.1176 |
- sickle emmean SE df lower.CL upper.CL
- NORM 32.34204 0.0976982 96 32.14811 32.53597
- HET 32.42074 0.2157666 96 31.99245 32.84904

| (Intercept) |
32.35 |
0.09899 |
326.8 |
2.28e-149 |
* * * |
| sickleHET |
0.01815 |
0.2389 |
0.07598 |
0.9396 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 99 |
0.8964 |
5.951e-05 |
-0.01025 |
| (Intercept) |
32.16 |
32.55 |
| sickleHET |
-0.456 |
0.4923 |
- sickle emmean SE df lower.CL upper.CL
- NORM 32.35244 0.0989890 97 32.15597 32.54890
- HET 32.37059 0.2174049 97 31.93910 32.80208

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
| (Intercept) |
33.17 |
0.05406 |
613.7 |
0 |
* * * |
| sickleHET |
-0.2413 |
0.1335 |
-1.807 |
0.07142 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
| 476 |
1.078 |
0.00684 |
0.004745 |
| (Intercept) |
33.07 |
33.28 |
| sickleHET |
-0.5037 |
0.02112 |
| (Intercept) |
32.69 |
0.1244 |
262.7 |
0 |
* * * |
| sickleHET |
-0.2416 |
0.1311 |
-1.843 |
0.06598 |
|
| age_at_collection_years_2010 |
0.06428 |
0.01483 |
4.334 |
1.791e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
| 476 |
1.059 |
0.04477 |
0.04073 |
| (Intercept) |
32.44 |
32.93 |
| sickleHET |
-0.4992 |
0.01602 |
| age_at_collection_years_2010 |
0.03514 |
0.09343 |
sickle _ NORM ________________________________________________________________
| (Intercept) |
32.72 |
0.1396 |
234.4 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.08386 |
0.1529 |
-0.5484 |
0.5837 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1449 |
0.1564 |
-0.926 |
0.355 |
|
| age_at_collection_years_2010 |
0.06499 |
0.01652 |
3.935 |
9.825e-05 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 398 |
1.082 |
0.03928 |
0.03196 |
| (Intercept) |
32.44 |
32.99 |
| g6pd_202_rtpcrHET |
-0.3845 |
0.2168 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4524 |
0.1627 |
| age_at_collection_years_2010 |
0.03252 |
0.09746 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.20859 0.0647842 394 33.08122 33.33595
- HET 33.12473 0.1385064 394 32.85242 33.39703
- HOM/HEMI 33.06373 0.1422872 394 32.78399 33.34347
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
33.2 |
0.06594 |
503.5 |
0 |
* * * |
| g6pd_202_rtpcrHET |
-0.0792 |
0.1557 |
-0.5087 |
0.6112 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.1056 |
0.1589 |
-0.6644 |
0.5068 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 398 |
1.101 |
0.001517 |
-0.003539 |
| (Intercept) |
33.07 |
33.33 |
| g6pd_202_rtpcrHET |
-0.3853 |
0.2269 |
| g6pd_202_rtpcrHOM/HEMI |
-0.4181 |
0.2069 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.20215 0.0659404 395 33.07251 33.33179
- HET 33.12295 0.1410225 395 32.84570 33.40020
- HOM/HEMI 33.09655 0.1446237 395 32.81222 33.38088
| NORM |
279 |
| HET |
61 |
| HOM/HEMI |
58 |

| (Intercept) |
33.09 |
0.1375 |
240.6 |
0 |
* * * |
| thalHET |
-0.4682 |
0.1108 |
-4.225 |
2.973e-05 |
* * * |
| thalHOM |
-1.358 |
0.1371 |
-9.904 |
8.476e-21 |
* * * |
| age_at_collection_years_2010 |
0.07631 |
0.01481 |
5.152 |
4.09e-07 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 398 |
0.9689 |
0.2294 |
0.2235 |
| (Intercept) |
32.82 |
33.36 |
| thalHET |
-0.6861 |
-0.2503 |
| thalHOM |
-1.628 |
-1.088 |
| age_at_collection_years_2010 |
0.04719 |
0.1054 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.67324 0.0850790 394 33.50597 33.84050
- HET 33.20500 0.0710407 394 33.06533 33.34466
- HOM 32.31524 0.1072361 394 32.10442 32.52607

| (Intercept) |
33.65 |
0.08768 |
383.8 |
0 |
* * * |
| thalHET |
-0.4478 |
0.1143 |
-3.918 |
0.0001052 |
* * * |
| thalHOM |
-1.299 |
0.141 |
-9.215 |
1.874e-18 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 398 |
0.9997 |
0.1774 |
0.1733 |
| (Intercept) |
33.48 |
33.82 |
| thalHET |
-0.6725 |
-0.2231 |
| thalHOM |
-1.576 |
-1.022 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.65154 0.0876788 395 33.47916 33.82391
- HET 33.20376 0.0733010 395 33.05965 33.34787
- HOM 32.35244 0.1103975 395 32.13540 32.56948

sickle _ HET ________________________________________________________________
| (Intercept) |
32.56 |
0.3007 |
108.3 |
2.995e-83 |
* * * |
| g6pd_202_rtpcrHET |
-0.1852 |
0.2898 |
-0.6389 |
0.5249 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.4579 |
0.3021 |
-1.516 |
0.1339 |
|
| age_at_collection_years_2010 |
0.06337 |
0.03433 |
1.846 |
0.06893 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
| 78 |
0.9433 |
0.07766 |
0.04027 |
| (Intercept) |
31.96 |
33.16 |
| g6pd_202_rtpcrHET |
-0.7626 |
0.3923 |
| g6pd_202_rtpcrHOM/HEMI |
-1.06 |
0.1441 |
| age_at_collection_years_2010 |
-0.005041 |
0.1318 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.03701 0.1312090 74 32.77557 33.29845
- HET 32.85185 0.2565379 74 32.34069 33.36302
- HOM/HEMI 32.57912 0.2725477 74 32.03606 33.12218
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
33.06 |
0.1329 |
248.7 |
3.788e-111 |
* * * |
| g6pd_202_rtpcrHET |
-0.2915 |
0.2886 |
-1.01 |
0.3157 |
|
| g6pd_202_rtpcrHOM/HEMI |
-0.4558 |
0.3069 |
-1.485 |
0.1417 |
|
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
| 78 |
0.9583 |
0.0352 |
0.00947 |
| (Intercept) |
32.79 |
33.32 |
| g6pd_202_rtpcrHET |
-0.8663 |
0.2833 |
| g6pd_202_rtpcrHOM/HEMI |
-1.067 |
0.1556 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 33.05577 0.1328971 75 32.79102 33.32051
- HET 32.76429 0.2561258 75 32.25406 33.27451
- HOM/HEMI 32.60000 0.2766474 75 32.04889 33.15111
| NORM |
52 |
| HET |
14 |
| HOM/HEMI |
12 |

| (Intercept) |
32.86 |
0.2848 |
115.4 |
2.761e-85 |
* * * |
| thalHET |
-0.5475 |
0.2314 |
-2.366 |
0.02058 |
* |
| thalHOM |
-0.9845 |
0.2751 |
-3.579 |
0.0006138 |
* * * |
| age_at_collection_years_2010 |
0.06972 |
0.03198 |
2.18 |
0.03243 |
* |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
| 78 |
0.882 |
0.1936 |
0.1609 |
| (Intercept) |
32.3 |
33.43 |
| thalHET |
-1.009 |
-0.08649 |
| thalHOM |
-1.533 |
-0.4364 |
| age_at_collection_years_2010 |
0.005997 |
0.1334 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.39360 0.1737603 74 33.04737 33.73982
- HET 32.84607 0.1505332 74 32.54612 33.14601
- HOM 32.40906 0.2146477 74 31.98137 32.83676

| (Intercept) |
33.36 |
0.1773 |
188.2 |
4.452e-102 |
* * * |
| thalHET |
-0.4663 |
0.234 |
-1.993 |
0.04995 |
* |
| thalHOM |
-0.9871 |
0.2819 |
-3.502 |
0.0007828 |
* * * |
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
| 78 |
0.9038 |
0.1419 |
0.119 |
| (Intercept) |
33 |
33.71 |
| thalHET |
-0.9324 |
-0.0001032 |
| thalHOM |
-1.549 |
-0.4255 |
- thal emmean SE df lower.CL upper.CL
- NORM 33.35769 0.1772528 75 33.00459 33.71080
- HET 32.89143 0.1527727 75 32.58709 33.19577
- HOM 32.37059 0.2192075 75 31.93390 32.80727

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
##Association of g6pd enzyme activity with each polymorphism in all individuals {.tabset}
u_rcc
g6pd_202_rtpcr + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9798 |
1.596e-05 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9739 |
1.105e-05 * * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 3.593 |
2 |
0.1659 |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 0.7703 |
2 |
0.6804 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 97.13 |
2 |
8.119e-22 * * * |
| (Intercept) |
175.1 |
3.364 |
52.06 |
8.732e-183 |
| b[, rbc_polymorphism[i]]HET |
-44.35 |
7.801 |
-5.685 |
2.487e-08 |
| b[, rbc_polymorphism[i]]HOM/HEMI |
-127 |
9.629 |
-13.19 |
2.244e-33 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
58.47 |
0.3144 |
0.3111 |
| (Intercept) |
168.5 |
181.8 |
| b[, rbc_polymorphism[i]]HET |
-59.69 |
-29.02 |
| b[, rbc_polymorphism[i]]HOM/HEMI |
-146 |
-108.1 |
| NORM |
302 |
| HET |
69 |
| HOM/HEMI |
42 |
| (Intercept) |
198.2 |
9.147 |
21.67 |
1.86e-66 |
| g6pd_202_rtpcrHET |
-49.97 |
8.549 |
-5.844 |
1.173e-08 |
| g6pd_202_rtpcrHOM/HEMI |
-136.3 |
9.851 |
-13.83 |
5.75e-35 |
| age_at_collection_years_2010 |
-1.066 |
0.9925 |
-1.074 |
0.2837 |
| ethnicDigo |
66.88 |
41.15 |
1.625 |
0.105 |
| ethnicDurum |
-11.65 |
41.11 |
-0.2834 |
0.7771 |
| ethnicGiriama |
-18.7 |
6.83 |
-2.738 |
0.006507 |
| ethnicJibana |
-14.2 |
13.15 |
-1.08 |
0.2809 |
| ethnicKambe |
-34.36 |
41.1 |
-0.836 |
0.4037 |
| ethnicKauma |
-9.943 |
22.55 |
-0.441 |
0.6595 |
| ethnicRabai |
18.81 |
41.41 |
0.4542 |
0.65 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
57.52 |
0.383 |
0.3653 |
| (Intercept) |
180.2 |
216.2 |
| g6pd_202_rtpcrHET |
-66.78 |
-33.15 |
| g6pd_202_rtpcrHOM/HEMI |
-155.6 |
-116.9 |
| age_at_collection_years_2010 |
-3.018 |
0.8864 |
| ethnicDigo |
-14.05 |
147.8 |
| ethnicDurum |
-92.49 |
69.2 |
| ethnicGiriama |
-32.13 |
-5.264 |
| ethnicJibana |
-40.05 |
11.66 |
| ethnicKambe |
-115.2 |
46.48 |
| ethnicKauma |
-54.29 |
34.4 |
| ethnicRabai |
-62.64 |
100.3 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 189.54356 10.86986 347 168.16447 210.92266
- HET 139.57741 12.68905 347 114.62027 164.53454
- HOM/HEMI 53.27477 13.99091 347 25.75712 80.79242
| NORM |
302 |
| HET |
69 |
| HOM/HEMI |
42 |
thal + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9798 |
1.596e-05 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9739 |
1.105e-05 * * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.5709 |
2 |
0.7517 |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 0.7703 |
2 |
0.6804 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.5567 |
2 |
0.757 |
| (Intercept) |
160.6 |
6.067 |
26.47 |
8.978e-91 |
| b[, rbc_polymorphism[i]]HET |
-9.239 |
7.926 |
-1.166 |
0.2445 |
| b[, rbc_polymorphism[i]]HOM |
-7.264 |
9.692 |
-0.7496 |
0.454 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
70.49 |
0.003418 |
-0.001444 |
| (Intercept) |
148.7 |
172.6 |
| b[, rbc_polymorphism[i]]HET |
-24.82 |
6.342 |
| b[, rbc_polymorphism[i]]HOM |
-26.32 |
11.79 |
| (Intercept) |
177.2 |
12.09 |
14.65 |
3.457e-38 |
| thalHET |
-10.86 |
8.8 |
-1.234 |
0.218 |
| thalHOM |
-13.41 |
11 |
-1.219 |
0.2235 |
| age_at_collection_years_2010 |
-1.29 |
1.251 |
-1.031 |
0.3032 |
| ethnicDigo |
75.86 |
51.81 |
1.464 |
0.144 |
| ethnicDurum |
-1.648 |
51.72 |
-0.03186 |
0.9746 |
| ethnicGiriama |
-7.496 |
8.512 |
-0.8806 |
0.3792 |
| ethnicJibana |
-0.1585 |
16.53 |
-0.00959 |
0.9924 |
| ethnicKambe |
1.937 |
52.21 |
0.03709 |
0.9704 |
| ethnicKauma |
-22.01 |
28.3 |
-0.778 |
0.4371 |
| ethnicRabai |
40.25 |
52.23 |
0.7706 |
0.4414 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
72.35 |
0.02384 |
-0.004294 |
| (Intercept) |
153.4 |
201 |
| thalHET |
-28.17 |
6.449 |
| thalHOM |
-35.03 |
8.218 |
| age_at_collection_years_2010 |
-3.751 |
1.171 |
| ethnicDigo |
-26.04 |
177.8 |
| ethnicDurum |
-103.4 |
100.1 |
| ethnicGiriama |
-24.24 |
9.246 |
| ethnicJibana |
-32.66 |
32.35 |
| ethnicKambe |
-100.8 |
104.6 |
| ethnicKauma |
-77.67 |
33.64 |
| ethnicRabai |
-62.48 |
143 |
- thal emmean SE df lower.CL upper.CL
- NORM 178.0154 14.64237 347 149.2165 206.8144
- HET 167.1570 14.16498 347 139.2970 195.0170
- HOM 164.6073 15.25043 347 134.6124 194.6022
sickle + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9798 |
1.596e-05 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9739 |
1.105e-05 * * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.6778 |
1 |
0.4104 |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 0.7703 |
2 |
0.6804 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.7254 |
1 |
0.3944 |
| (Intercept) |
153.9 |
3.747 |
41.08 |
1.309e-147 |
| b[, rbc_polymorphism[i]]HET |
6.213 |
9.913 |
0.6268 |
0.5312 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
70.5 |
0.0009549 |
-0.001476 |
| (Intercept) |
146.6 |
161.3 |
| b[, rbc_polymorphism[i]]HET |
-13.27 |
25.7 |
| (Intercept) |
170 |
11.22 |
15.15 |
3.437e-40 |
| sickleHET |
4.391 |
11.4 |
0.3852 |
0.7003 |
| age_at_collection_years_2010 |
-1.423 |
1.25 |
-1.139 |
0.2556 |
| ethnicDigo |
71.21 |
51.88 |
1.373 |
0.1707 |
| ethnicDurum |
-5.523 |
51.69 |
-0.1068 |
0.915 |
| ethnicGiriama |
-7.602 |
8.519 |
-0.8923 |
0.3728 |
| ethnicJibana |
-2.012 |
16.68 |
-0.1206 |
0.9041 |
| ethnicKambe |
-7.581 |
52.59 |
-0.1442 |
0.8855 |
| ethnicKauma |
-18.79 |
28.31 |
-0.6637 |
0.5073 |
| ethnicRabai |
45.49 |
52.23 |
0.8709 |
0.3844 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
72.44 |
0.0185 |
-0.00688 |
| (Intercept) |
147.9 |
192.1 |
| sickleHET |
-18.03 |
26.81 |
| age_at_collection_years_2010 |
-3.881 |
1.035 |
| ethnicDigo |
-30.82 |
173.2 |
| ethnicDurum |
-107.2 |
96.14 |
| ethnicGiriama |
-24.36 |
9.154 |
| ethnicJibana |
-34.82 |
30.8 |
| ethnicKambe |
-111 |
95.85 |
| ethnicKauma |
-74.47 |
36.89 |
| ethnicRabai |
-57.24 |
148.2 |
- sickle emmean SE df lower.CL upper.CL
- NORM 168.3653 13.98098 348 140.8675 195.8632
- HET 172.7565 15.51322 348 142.2450 203.2679
####Univariate association of g6pd enzyme activity with age, sex, malaria
| (Intercept) |
168 |
8.605 |
19.52 |
1.552e-60 |
* * * |
| age_at_collection_years_2010 |
-1.741 |
1.041 |
-1.673 |
0.09507 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, age_at_collection_years))
| 413 |
70.29 |
0.006765 |
0.004348 |
| (Intercept) |
151.1 |
184.9 |
| age_at_collection_years_2010 |
-3.787 |
0.3046 |
| (Intercept) |
161.5 |
4.856 |
33.25 |
1.334e-118 |
* * * |
| sexMALE |
-13.49 |
6.91 |
-1.953 |
0.05153 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, “sex”))
| 413 |
70.2 |
0.009192 |
0.006781 |
| (Intercept) |
151.9 |
171 |
| sexMALE |
-27.07 |
0.09019 |
| (Intercept) |
160.5 |
6.608 |
24.28 |
4.688e-77 |
* * * |
| ethnicDigo |
69.77 |
51.61 |
1.352 |
0.1773 |
|
| ethnicDurum |
-7.56 |
51.61 |
-0.1465 |
0.8836 |
|
| ethnicGiriama |
-9.527 |
8.358 |
-1.14 |
0.2551 |
|
| ethnicJibana |
-0.0934 |
16.48 |
-0.005669 |
0.9955 |
|
| ethnicKambe |
-5.478 |
51.61 |
-0.1061 |
0.9155 |
|
| ethnicKauma |
-16.76 |
28.15 |
-0.5956 |
0.5518 |
|
| ethnicRabai |
54.54 |
51.61 |
1.057 |
0.2914 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, “ethnic”))
| 358 |
72.39 |
0.01446 |
-0.005252 |
| (Intercept) |
147.5 |
173.5 |
| ethnicDigo |
-31.73 |
171.3 |
| ethnicDurum |
-109.1 |
93.94 |
| ethnicGiriama |
-25.97 |
6.912 |
| ethnicJibana |
-32.5 |
32.31 |
| ethnicKambe |
-107 |
96.03 |
| ethnicKauma |
-72.12 |
38.59 |
| ethnicRabai |
-46.97 |
156 |
| Chonyi |
120 |
| Digo |
2 |
| Durum |
2 |
| Giriama |
200 |
| Jibana |
23 |
| Kambe |
2 |
| Kauma |
7 |
| Rabai |
2 |
| (Intercept) |
152.8 |
3.886 |
39.33 |
3.061e-141 |
* * * |
| malaria_statusassymptomatic_malaria |
6.727 |
10.43 |
0.6448 |
0.5194 |
|
| malaria_statusuncomplicated_malaria |
14.83 |
13.24 |
1.12 |
0.2635 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, malariapositive))
| 413 |
70.48 |
0.003718 |
-0.001142 |
| (Intercept) |
145.2 |
160.5 |
| malaria_statusassymptomatic_malaria |
-13.78 |
27.23 |
| malaria_statusuncomplicated_malaria |
-11.2 |
40.86 |
| no_malaria |
329 |
| assymptomatic_malaria |
53 |
| uncomplicated_malaria |
31 |
####Association of g6pd enzyme activity with each polymorphism and age (and interactions between them)
Shapiro-Wilk normality test: pgd_genopheno_01042018[, ed[j]]
| 0.9798 |
1.596e-05 * * * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
| 4.872 |
1 |
0.0273 * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", "g6pd_202_rtpcr"]
| 7.006 |
1 |
0.008123 * * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
| 3.241 |
1 |
0.07181 |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "sickle"]
| 1.417 |
1 |
0.234 |
u_ghb3
g6pd_202_rtpcr + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9864 |
0.0006674 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9845 |
0.001283 * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 6.705 |
2 |
0.03499 * |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 1.171 |
2 |
0.5569 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 108.9 |
2 |
2.273e-24 * * * |
| (Intercept) |
7.438 |
0.1478 |
50.32 |
1.516e-177 |
| b[, rbc_polymorphism[i]]HET |
-2.168 |
0.3428 |
-6.325 |
6.619e-10 |
| b[, rbc_polymorphism[i]]HOM/HEMI |
-5.627 |
0.423 |
-13.3 |
8.25e-34 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
2.569 |
0.3235 |
0.3202 |
| (Intercept) |
7.147 |
7.728 |
| b[, rbc_polymorphism[i]]HET |
-2.842 |
-1.494 |
| b[, rbc_polymorphism[i]]HOM/HEMI |
-6.458 |
-4.795 |
| NORM |
302 |
| HET |
69 |
| HOM/HEMI |
42 |
| (Intercept) |
8.814 |
0.3905 |
22.57 |
4.679e-70 |
| g6pd_202_rtpcrHET |
-2.355 |
0.365 |
-6.451 |
3.73e-10 |
| g6pd_202_rtpcrHOM/HEMI |
-5.902 |
0.4205 |
-14.04 |
9.363e-36 |
| age_at_collection_years_2010 |
-0.1215 |
0.04237 |
-2.869 |
0.004374 |
| ethnicDigo |
2.866 |
1.757 |
1.631 |
0.1037 |
| ethnicDurum |
0.1389 |
1.755 |
0.07918 |
0.9369 |
| ethnicGiriama |
-0.6708 |
0.2916 |
-2.301 |
0.022 |
| ethnicJibana |
-0.3902 |
0.5612 |
-0.6953 |
0.4873 |
| ethnicKambe |
-0.6613 |
1.755 |
-0.3769 |
0.7065 |
| ethnicKauma |
0.4366 |
0.9626 |
0.4536 |
0.6504 |
| ethnicRabai |
0.1766 |
1.768 |
0.09988 |
0.9205 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
2.456 |
0.4022 |
0.385 |
| (Intercept) |
8.046 |
9.582 |
| g6pd_202_rtpcrHET |
-3.072 |
-1.637 |
| g6pd_202_rtpcrHOM/HEMI |
-6.729 |
-5.075 |
| age_at_collection_years_2010 |
-0.2049 |
-0.03821 |
| ethnicDigo |
-0.5893 |
6.321 |
| ethnicDurum |
-3.312 |
3.59 |
| ethnicGiriama |
-1.244 |
-0.09736 |
| ethnicJibana |
-1.494 |
0.7136 |
| ethnicKambe |
-4.112 |
2.79 |
| ethnicKauma |
-1.457 |
2.33 |
| ethnicRabai |
-3.3 |
3.654 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 8.106799 0.4640401 347 7.194113 9.019484
- HET 5.752241 0.5417026 347 4.686807 6.817674
- HOM/HEMI 2.204419 0.5972794 347 1.029675 3.379162
| NORM |
302 |
| HET |
69 |
| HOM/HEMI |
42 |
thal + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9864 |
0.0006674 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9845 |
0.001283 * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 2.53 |
2 |
0.2823 |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 1.171 |
2 |
0.5569 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 8.596 |
2 |
0.0136 * |
| (Intercept) |
6.224 |
0.2663 |
23.37 |
2.14e-77 |
| b[, rbc_polymorphism[i]]HET |
0.1086 |
0.3479 |
0.312 |
0.7552 |
| b[, rbc_polymorphism[i]]HOM |
1.089 |
0.4254 |
2.56 |
0.01083 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
3.094 |
0.01829 |
0.0135 |
| (Intercept) |
5.7 |
6.747 |
| b[, rbc_polymorphism[i]]HET |
-0.5754 |
0.7925 |
| b[, rbc_polymorphism[i]]HOM |
0.2527 |
1.925 |
| (Intercept) |
7.439 |
0.5197 |
14.31 |
7.704e-37 |
| thalHET |
0.06637 |
0.3783 |
0.1755 |
0.8608 |
| thalHOM |
0.8384 |
0.4726 |
1.774 |
0.07696 |
| age_at_collection_years_2010 |
-0.1425 |
0.05379 |
-2.649 |
0.008452 |
| ethnicDigo |
3.191 |
2.227 |
1.433 |
0.1528 |
| ethnicDurum |
0.05463 |
2.223 |
0.02457 |
0.9804 |
| ethnicGiriama |
-0.2151 |
0.3659 |
-0.588 |
0.5569 |
| ethnicJibana |
0.2012 |
0.7104 |
0.2832 |
0.7772 |
| ethnicKambe |
0.04932 |
2.244 |
0.02198 |
0.9825 |
| ethnicKauma |
0.2075 |
1.216 |
0.1706 |
0.8646 |
| ethnicRabai |
1.591 |
2.245 |
0.7087 |
0.479 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
3.11 |
0.04113 |
0.0135 |
| (Intercept) |
6.417 |
8.461 |
| thalHET |
-0.6776 |
0.8103 |
| thalHOM |
-0.09118 |
1.768 |
| age_at_collection_years_2010 |
-0.2482 |
-0.03667 |
| ethnicDigo |
-1.189 |
7.571 |
| ethnicDurum |
-4.318 |
4.428 |
| ethnicGiriama |
-0.9348 |
0.5045 |
| ethnicJibana |
-1.196 |
1.598 |
| ethnicKambe |
-4.365 |
4.463 |
| ethnicKauma |
-2.185 |
2.6 |
| ethnicRabai |
-2.825 |
6.007 |
- thal emmean SE df lower.CL upper.CL
- NORM 6.967416 0.6294013 347 5.729495 8.205338
- HET 7.033784 0.6088811 347 5.836222 8.231346
- HOM 7.805847 0.6555390 347 6.516517 9.095177
sickle + malaria +ve
Shapiro-Wilk normality test: b[, ed[j]]
| 0.9864 |
0.0006674 * * * |
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
| 0.9845 |
0.001283 * * |
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.4735 |
1 |
0.4914 |
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
| 1.171 |
2 |
0.5569 |
Kruskal walis___________________________________________________________________
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
| 0.8716 |
1 |
0.3505 |
| (Intercept) |
6.457 |
0.1657 |
38.97 |
4.152e-140 |
| b[, rbc_polymorphism[i]]HET |
0.3275 |
0.4383 |
0.7471 |
0.4555 |
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
| 413 |
3.117 |
0.001356 |
-0.001074 |
| (Intercept) |
6.131 |
6.782 |
| b[, rbc_polymorphism[i]]HET |
-0.5342 |
1.189 |
| (Intercept) |
7.541 |
0.4833 |
15.6 |
5.522e-42 |
| sickleHET |
0.2553 |
0.4911 |
0.5199 |
0.6035 |
| age_at_collection_years_2010 |
-0.1369 |
0.05383 |
-2.544 |
0.01139 |
| ethnicDigo |
2.976 |
2.235 |
1.332 |
0.1839 |
| ethnicDurum |
0.3595 |
2.227 |
0.1614 |
0.8719 |
| ethnicGiriama |
-0.1699 |
0.367 |
-0.4628 |
0.6438 |
| ethnicJibana |
0.124 |
0.7187 |
0.1726 |
0.8631 |
| ethnicKambe |
0.4839 |
2.265 |
0.2136 |
0.831 |
| ethnicKauma |
0.1041 |
1.22 |
0.08537 |
0.932 |
| ethnicRabai |
1.351 |
2.25 |
0.6002 |
0.5488 |
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
| 358 |
3.121 |
0.03163 |
0.00659 |
| (Intercept) |
6.591 |
8.492 |
| sickleHET |
-0.7106 |
1.221 |
| age_at_collection_years_2010 |
-0.2428 |
-0.03107 |
| ethnicDigo |
-1.42 |
7.371 |
| ethnicDurum |
-4.02 |
4.739 |
| ethnicGiriama |
-0.8917 |
0.552 |
| ethnicJibana |
-1.289 |
1.537 |
| ethnicKambe |
-3.972 |
4.94 |
| ethnicKauma |
-2.295 |
2.503 |
| ethnicRabai |
-3.075 |
5.776 |
- sickle emmean SE df lower.CL upper.CL
- NORM 7.131412 0.6022981 348 5.946809 8.316014
- HET 7.386741 0.6683065 348 6.072313 8.701169
####Univariate association of g6pd enzyme activity with age, sex, malaria
| (Intercept) |
7.746 |
0.376 |
20.6 |
2.633e-65 |
* * * |
| age_at_collection_years_2010 |
-0.1642 |
0.04547 |
-3.611 |
0.0003428 |
* * * |
Fitting linear model: as.formula(paste(ed[j], “~”, age_at_collection_years))
| 413 |
3.071 |
0.03075 |
0.02839 |
| (Intercept) |
7.007 |
8.485 |
| age_at_collection_years_2010 |
-0.2536 |
-0.0748 |
| (Intercept) |
6.788 |
0.2148 |
31.59 |
4.987e-112 |
* * * |
| sexMALE |
-0.5757 |
0.3057 |
-1.883 |
0.06035 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, “sex”))
| 413 |
3.106 |
0.008557 |
0.006145 |
| (Intercept) |
6.365 |
7.21 |
| sexMALE |
-1.177 |
0.02517 |
| (Intercept) |
6.598 |
0.2868 |
23.01 |
5.395e-72 |
* * * |
| ethnicDigo |
2.777 |
2.24 |
1.24 |
0.2159 |
|
| ethnicDurum |
0.1872 |
2.24 |
0.08356 |
0.9335 |
|
| ethnicGiriama |
-0.3499 |
0.3628 |
-0.9643 |
0.3355 |
|
| ethnicJibana |
0.2742 |
0.7152 |
0.3834 |
0.7016 |
|
| ethnicKambe |
0.5427 |
2.24 |
0.2423 |
0.8087 |
|
| ethnicKauma |
0.3226 |
1.222 |
0.264 |
0.7919 |
|
| ethnicRabai |
2.161 |
2.24 |
0.9649 |
0.3353 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, “ethnic”))
| 358 |
3.142 |
0.01296 |
-0.006781 |
| (Intercept) |
6.034 |
7.162 |
| ethnicDigo |
-1.628 |
7.183 |
| ethnicDurum |
-4.218 |
4.593 |
| ethnicGiriama |
-1.063 |
0.3637 |
| ethnicJibana |
-1.132 |
1.681 |
| ethnicKambe |
-3.863 |
4.948 |
| ethnicKauma |
-2.08 |
2.725 |
| ethnicRabai |
-2.244 |
6.567 |
| Chonyi |
120 |
| Digo |
2 |
| Durum |
2 |
| Giriama |
200 |
| Jibana |
23 |
| Kambe |
2 |
| Kauma |
7 |
| Rabai |
2 |
| (Intercept) |
6.411 |
0.1719 |
37.3 |
7.661e-134 |
* * * |
| malaria_statusassymptomatic_malaria |
0.4357 |
0.4614 |
0.9442 |
0.3456 |
|
| malaria_statusuncomplicated_malaria |
0.489 |
0.5857 |
0.8349 |
0.4042 |
|
Fitting linear model: as.formula(paste(ed[j], “~”, malariapositive))
| 413 |
3.118 |
0.003484 |
-0.001377 |
| (Intercept) |
6.073 |
6.749 |
| malaria_statusassymptomatic_malaria |
-0.4714 |
1.343 |
| malaria_statusuncomplicated_malaria |
-0.6624 |
1.64 |
| no_malaria |
329 |
| assymptomatic_malaria |
53 |
| uncomplicated_malaria |
31 |
####Association of g6pd enzyme activity with each polymorphism and age (and interactions between them)
Shapiro-Wilk normality test: pgd_genopheno_01042018[, ed[j]]
| 0.9864 |
0.0006674 * * * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
| 3.881 |
1 |
0.04883 * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", "g6pd_202_rtpcr"]
| 7.424 |
1 |
0.006435 * * |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
| 2.725 |
1 |
0.09879 |
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "sickle"]
| 1.417 |
1 |
0.234 |
| (Intercept) |
15.75 |
1.611 |
9.773 |
4.538e-20 |
* * * |
| g6pd_202_rtpcrHET |
-2.07 |
0.3575 |
-5.792 |
1.569e-08 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-5.389 |
0.4233 |
-12.73 |
1.079e-30 |
* * * |
| thalHET |
-0.1266 |
0.306 |
-0.4138 |
0.6793 |
|
| thalHOM |
-0.3213 |
0.4511 |
-0.7122 |
0.4768 |
|
| age_at_collection_years_2010 |
-0.05983 |
0.04366 |
-1.37 |
0.1714 |
|
| ethnicDigo |
2.661 |
1.702 |
1.563 |
0.1189 |
|
| ethnicDurum |
-0.4603 |
1.7 |
-0.2707 |
0.7868 |
|
| ethnicGiriama |
-0.7218 |
0.2819 |
-2.56 |
0.01089 |
* |
| ethnicJibana |
-0.4876 |
0.5436 |
-0.8969 |
0.3704 |
|
| ethnicKambe |
-1.082 |
1.714 |
-0.631 |
0.5285 |
|
| ethnicKauma |
-0.2913 |
0.9523 |
-0.3059 |
0.7599 |
|
| ethnicRabai |
0.1789 |
1.716 |
0.1043 |
0.917 |
|
| mcv_2010 |
-0.09934 |
0.02149 |
-4.622 |
5.372e-06 |
* * * |
Fitting linear model: as.formula(paste(“u_ghb3”, “~”, “g6pd_202_rtpcr”, “+”, “thal”, “+”, age_at_collection_years, “+”, “ethnic”, “+”, “mcv_2010”))
| 358 |
2.371 |
0.4473 |
0.4264 |
| (Intercept) |
12.58 |
18.92 |
| g6pd_202_rtpcrHET |
-2.774 |
-1.367 |
| g6pd_202_rtpcrHOM/HEMI |
-6.222 |
-4.557 |
| thalHET |
-0.7286 |
0.4753 |
| thalHOM |
-1.209 |
0.566 |
| age_at_collection_years_2010 |
-0.1457 |
0.02604 |
| ethnicDigo |
-0.6867 |
6.009 |
| ethnicDurum |
-3.804 |
2.884 |
| ethnicGiriama |
-1.276 |
-0.1673 |
| ethnicJibana |
-1.557 |
0.5816 |
| ethnicKambe |
-4.454 |
2.29 |
| ethnicKauma |
-2.164 |
1.582 |
| ethnicRabai |
-3.196 |
3.554 |
| mcv_2010 |
-0.1416 |
-0.05707 |
- g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
- NORM NORM 7.921991 0.4877363 344 6.9626706 8.881312
- HET NORM 5.851545 0.5658453 344 4.7385932 6.964497
- HOM/HEMI NORM 2.532716 0.6342709 344 1.2851790 3.780254
- NORM HET 7.795349 0.4757823 344 6.8595404 8.731158
- HET HET 5.724903 0.5397260 344 4.6633244 6.786481
- HOM/HEMI HET 2.406074 0.5945318 344 1.2366988 3.575449
- NORM HOM 7.600664 0.5530672 344 6.5128454 8.688484
- HET HOM 5.530218 0.6030670 344 4.3440555 6.716381
- HOM/HEMI HOM 2.211390 0.6319130 344 0.9684899 3.454289

| (Intercept) |
237.9 |
29.32 |
8.114 |
5.828e-15 |
* * * |
| g6pd_202_rtpcrHET |
-50.45 |
7.611 |
-6.629 |
1.075e-10 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-135.2 |
9.499 |
-14.23 |
1.329e-37 |
* * * |
| hgb_2010 |
-16.72 |
2.707 |
-6.177 |
1.583e-09 |
* * * |
| mcv_2010 |
1.731 |
0.4217 |
4.104 |
4.914e-05 |
* * * |
Fitting linear model: as.formula(paste(“u_rcc”, “~”, “g6pd_202_rtpcr”, “+”, “hgb_2010”, “+”, “mcv_2010”))
| 413 |
56 |
0.3742 |
0.3681 |
| (Intercept) |
180.2 |
295.5 |
| g6pd_202_rtpcrHET |
-65.41 |
-35.49 |
| g6pd_202_rtpcrHOM/HEMI |
-153.9 |
-116.5 |
| hgb_2010 |
-22.04 |
-11.4 |
| mcv_2010 |
0.9016 |
2.56 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 176.99634 3.256717 408 170.59431 183.3984
- HET 126.54807 6.811599 408 113.15786 139.9383
- HOM/HEMI 41.81346 8.827338 408 24.46072 59.1662

##wambua g6pd activity {.tabset}
g6pd_202_rtpcr
u_rcc
g6pd_202_rtpcr _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
304.2 |
42.23 |
7.202 |
7.024e-12 |
* * * |
| thalHET |
-4.704 |
7.776 |
-0.605 |
0.5458 |
|
| thalHOM |
-3.143 |
10.88 |
-0.2889 |
0.7729 |
|
| mcv_2010 |
1.513 |
0.5639 |
2.683 |
0.007781 |
* * |
| hgb_2010 |
-19.14 |
3.072 |
-6.233 |
1.947e-09 |
* * * |
| ethnicDigo |
27.33 |
51.57 |
0.53 |
0.5966 |
|
| ethnicDurum |
4.01 |
51.63 |
0.07766 |
0.9382 |
|
| ethnicGiriama |
-27.99 |
7.127 |
-3.927 |
0.0001112 |
* * * |
| ethnicJibana |
-13.08 |
13.79 |
-0.9489 |
0.3436 |
|
| ethnicKambe |
-16.53 |
37.26 |
-0.4436 |
0.6577 |
|
| ethnicKauma |
-14.79 |
24.27 |
-0.6091 |
0.543 |
|
| ethnicRabai |
11.11 |
37.02 |
0.3002 |
0.7643 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 261 |
51.1 |
0.1902 |
0.1544 |
| (Intercept) |
221 |
387.3 |
| thalHET |
-20.02 |
10.61 |
| thalHOM |
-24.57 |
18.29 |
| mcv_2010 |
0.4025 |
2.624 |
| hgb_2010 |
-25.19 |
-13.09 |
| ethnicDigo |
-74.25 |
128.9 |
| ethnicDurum |
-97.69 |
105.7 |
| ethnicGiriama |
-42.03 |
-13.95 |
| ethnicJibana |
-40.24 |
14.07 |
| ethnicKambe |
-89.92 |
56.86 |
| ethnicKauma |
-62.6 |
33.02 |
| ethnicRabai |
-61.81 |
84.03 |
- thal emmean SE df lower.CL upper.CL
- NORM 194.5230 12.51437 249 169.8755 219.1705
- HET 189.8186 12.12818 249 165.9317 213.7055
- HOM 191.3797 14.05533 249 163.6972 219.0622

| (Intercept) |
292.4 |
34.91 |
8.374 |
4.009e-15 |
* * * |
| sickleHET |
22.37 |
9.497 |
2.355 |
0.0193 |
* |
| mcv_2010 |
1.603 |
0.463 |
3.463 |
0.0006291 |
* * * |
| hgb_2010 |
-19.23 |
3.012 |
-6.384 |
8.324e-10 |
* * * |
| ethnicDigo |
6.667 |
51.45 |
0.1296 |
0.897 |
|
| ethnicDurum |
5.687 |
50.91 |
0.1117 |
0.9111 |
|
| ethnicGiriama |
-27.01 |
7.048 |
-3.832 |
0.0001606 |
* * * |
| ethnicJibana |
-13.72 |
13.61 |
-1.009 |
0.3142 |
|
| ethnicKambe |
-35.28 |
37.27 |
-0.9464 |
0.3448 |
|
| ethnicKauma |
-9.945 |
23.67 |
-0.4201 |
0.6748 |
|
| ethnicRabai |
6.271 |
36.33 |
0.1726 |
0.8631 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 261 |
50.48 |
0.2066 |
0.1748 |
| (Intercept) |
223.6 |
361.1 |
| sickleHET |
3.66 |
41.07 |
| mcv_2010 |
0.6913 |
2.515 |
| hgb_2010 |
-25.16 |
-13.3 |
| ethnicDigo |
-94.67 |
108 |
| ethnicDurum |
-94.57 |
105.9 |
| ethnicGiriama |
-40.89 |
-13.13 |
| ethnicJibana |
-40.52 |
13.08 |
| ethnicKambe |
-108.7 |
38.13 |
| ethnicKauma |
-56.57 |
36.68 |
| ethnicRabai |
-65.28 |
77.82 |
- sickle emmean SE df lower.CL upper.CL
- NORM 183.6327 11.98967 250 160.0191 207.2463
- HET 205.9982 12.94117 250 180.5106 231.4858

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
g6pd_202_rtpcr _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
18.54 |
1.747 |
10.61 |
6.047e-22 |
* * * |
| thalHET |
-0.3688 |
0.342 |
-1.078 |
0.282 |
|
| thalHOM |
-0.4045 |
0.4859 |
-0.8326 |
0.4059 |
|
| age_at_collection_years_2010 |
-0.1202 |
0.04886 |
-2.46 |
0.01458 |
* |
| mcv_2010 |
-0.128 |
0.02306 |
-5.551 |
7.258e-08 |
* * * |
| ethnicDigo |
1.201 |
2.269 |
0.5296 |
0.5969 |
|
| ethnicDurum |
0.7978 |
2.256 |
0.3537 |
0.7239 |
|
| ethnicGiriama |
-0.9314 |
0.3182 |
-2.927 |
0.003741 |
* * |
| ethnicJibana |
-0.2834 |
0.6015 |
-0.4712 |
0.6379 |
|
| ethnicKambe |
-1.355 |
1.625 |
-0.8339 |
0.4051 |
|
| ethnicKauma |
0.03406 |
1.059 |
0.03215 |
0.9744 |
|
| ethnicRabai |
-0.4652 |
1.631 |
-0.2853 |
0.7757 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 261 |
2.235 |
0.244 |
0.2106 |
| (Intercept) |
15.1 |
21.98 |
| thalHET |
-1.042 |
0.3049 |
| thalHOM |
-1.361 |
0.5524 |
| age_at_collection_years_2010 |
-0.2164 |
-0.02396 |
| mcv_2010 |
-0.1734 |
-0.08258 |
| ethnicDigo |
-3.267 |
5.669 |
| ethnicDurum |
-3.645 |
5.24 |
| ethnicGiriama |
-1.558 |
-0.3046 |
| ethnicJibana |
-1.468 |
0.9012 |
| ethnicKambe |
-4.556 |
1.846 |
| ethnicKauma |
-2.052 |
2.12 |
| ethnicRabai |
-3.677 |
2.746 |
- thal emmean SE df lower.CL upper.CL
- NORM 8.163421 0.5510722 249 7.078064 9.248778
- HET 7.794640 0.5310683 249 6.748682 8.840599
- HOM 7.758901 0.6155529 249 6.546546 8.971255

| (Intercept) |
17.34 |
1.318 |
13.16 |
2.127e-30 |
* * * |
| sickleHET |
1.059 |
0.4159 |
2.546 |
0.01151 |
* |
| age_at_collection_years_2010 |
-0.1353 |
0.04698 |
-2.88 |
0.004319 |
* * |
| mcv_2010 |
-0.1158 |
0.0182 |
-6.36 |
9.517e-10 |
* * * |
| ethnicDigo |
0.2809 |
2.26 |
0.1243 |
0.9012 |
|
| ethnicDurum |
0.8707 |
2.223 |
0.3916 |
0.6957 |
|
| ethnicGiriama |
-0.87 |
0.3151 |
-2.761 |
0.00619 |
* * |
| ethnicJibana |
-0.308 |
0.5934 |
-0.519 |
0.6042 |
|
| ethnicKambe |
-2.321 |
1.624 |
-1.429 |
0.1543 |
|
| ethnicKauma |
0.3417 |
1.034 |
0.3306 |
0.7412 |
|
| ethnicRabai |
-0.6372 |
1.606 |
-0.3968 |
0.6919 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 261 |
2.207 |
0.2594 |
0.2298 |
| (Intercept) |
14.75 |
19.94 |
| sickleHET |
0.2396 |
1.878 |
| age_at_collection_years_2010 |
-0.2279 |
-0.04279 |
| mcv_2010 |
-0.1516 |
-0.07991 |
| ethnicDigo |
-4.17 |
4.732 |
| ethnicDurum |
-3.508 |
5.25 |
| ethnicGiriama |
-1.491 |
-0.2494 |
| ethnicJibana |
-1.477 |
0.8608 |
| ethnicKambe |
-5.52 |
0.8783 |
| ethnicKauma |
-1.694 |
2.377 |
| ethnicRabai |
-3.8 |
2.526 |
- sickle emmean SE df lower.CL upper.CL
- NORM 7.534012 0.5257781 250 6.498493 8.569531
- HET 8.592614 0.5662281 250 7.477428 9.707799
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
g6pd_202_rtpcr _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
160.6 |
119.6 |
1.343 |
0.1856 |
|
| thalHET |
12.13 |
18.91 |
0.6411 |
0.5245 |
|
| thalHOM |
0.7476 |
30.88 |
0.02421 |
0.9808 |
|
| mcv_2010 |
2.572 |
1.699 |
1.514 |
0.1366 |
|
| hgb_2010 |
-18.81 |
9.137 |
-2.059 |
0.04499 |
* |
| ethnicDigo |
59.89 |
64.68 |
0.9259 |
0.3591 |
|
| ethnicDurum |
-60.71 |
67.69 |
-0.897 |
0.3742 |
|
| ethnicGiriama |
-30.92 |
17.99 |
-1.719 |
0.0921 |
|
| ethnicJibana |
-74.69 |
31.19 |
-2.395 |
0.02058 |
* |
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 57 |
60.97 |
0.2385 |
0.1115 |
| (Intercept) |
-79.83 |
401.1 |
| thalHET |
-25.9 |
50.15 |
| thalHOM |
-61.35 |
62.85 |
| mcv_2010 |
-0.8443 |
5.989 |
| hgb_2010 |
-37.18 |
-0.4377 |
| ethnicDigo |
-70.16 |
189.9 |
| ethnicDurum |
-196.8 |
75.38 |
| ethnicGiriama |
-67.09 |
5.251 |
| ethnicJibana |
-137.4 |
-11.98 |
- thal emmean SE df lower.CL upper.CL
- NORM 123.7734 24.96203 48 73.58389 173.9629
- HET 135.8987 21.34854 48 92.97456 178.8228
- HOM 124.5210 27.83654 48 68.55193 180.4902

| (Intercept) |
207.2 |
108.1 |
1.916 |
0.06127 |
|
| sickleHET |
-41.06 |
26.52 |
-1.548 |
0.128 |
|
| mcv_2010 |
2.603 |
1.438 |
1.811 |
0.07631 |
|
| hgb_2010 |
-22.16 |
8.845 |
-2.505 |
0.01562 |
* |
| ethnicDigo |
55.25 |
62.59 |
0.8828 |
0.3816 |
|
| ethnicDurum |
-77.08 |
62.8 |
-1.227 |
0.2256 |
|
| ethnicGiriama |
-32.33 |
16.81 |
-1.923 |
0.0603 |
|
| ethnicJibana |
-46.02 |
34.24 |
-1.344 |
0.185 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 57 |
59.23 |
0.2664 |
0.1616 |
| (Intercept) |
-10.17 |
424.5 |
| sickleHET |
-94.36 |
12.24 |
| mcv_2010 |
-0.2858 |
5.492 |
| hgb_2010 |
-39.93 |
-4.383 |
| ethnicDigo |
-70.52 |
181 |
| ethnicDurum |
-203.3 |
49.13 |
| ethnicGiriama |
-66.11 |
1.456 |
| ethnicJibana |
-114.8 |
22.78 |
- sickle emmean SE df lower.CL upper.CL
- NORM 136.45405 19.20863 49 97.85285 175.0552
- HET 95.39682 29.41710 49 36.28094 154.5127

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
g6pd_202_rtpcr _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
9.882 |
4.831 |
2.045 |
0.04631 |
* |
| thalHET |
0.4671 |
0.8063 |
0.5793 |
0.5651 |
|
| thalHOM |
-0.5208 |
1.371 |
-0.38 |
0.7057 |
|
| age_at_collection_years_2010 |
0.02927 |
0.124 |
0.236 |
0.8144 |
|
| mcv_2010 |
-0.05808 |
0.0644 |
-0.9019 |
0.3716 |
|
| ethnicDigo |
3.765 |
2.61 |
1.443 |
0.1556 |
|
| ethnicDurum |
-1.449 |
2.746 |
-0.5276 |
0.6002 |
|
| ethnicGiriama |
-1.05 |
0.7491 |
-1.401 |
0.1675 |
|
| ethnicJibana |
-2.262 |
1.286 |
-1.76 |
0.08482 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 57 |
2.531 |
0.1628 |
0.02323 |
| (Intercept) |
0.1684 |
19.6 |
| thalHET |
-1.154 |
2.088 |
| thalHOM |
-3.277 |
2.235 |
| age_at_collection_years_2010 |
-0.22 |
0.2786 |
| mcv_2010 |
-0.1876 |
0.0714 |
| ethnicDigo |
-1.483 |
9.012 |
| ethnicDurum |
-6.969 |
4.072 |
| ethnicGiriama |
-2.556 |
0.4564 |
| ethnicJibana |
-4.847 |
0.3225 |
- thal emmean SE df lower.CL upper.CL
- NORM 5.505709 1.0180115 48 3.458860 7.552558
- HET 5.972799 0.8372109 48 4.289473 7.656124
- HOM 4.984898 1.1990834 48 2.573979 7.395817

| (Intercept) |
9.805 |
4.097 |
2.393 |
0.02057 |
* |
| sickleHET |
-1.164 |
1.11 |
-1.048 |
0.2997 |
|
| age_at_collection_years_2010 |
-0.004102 |
0.1137 |
-0.03607 |
0.9714 |
|
| mcv_2010 |
-0.04935 |
0.0539 |
-0.9155 |
0.3644 |
|
| ethnicDigo |
3.869 |
2.556 |
1.514 |
0.1365 |
|
| ethnicDurum |
-2.147 |
2.627 |
-0.8172 |
0.4178 |
|
| ethnicGiriama |
-1.172 |
0.7097 |
-1.651 |
0.1052 |
|
| ethnicJibana |
-1.359 |
1.44 |
-0.9434 |
0.3501 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 57 |
2.501 |
0.165 |
0.04567 |
| (Intercept) |
1.572 |
18.04 |
| sickleHET |
-3.394 |
1.067 |
| age_at_collection_years_2010 |
-0.2326 |
0.2244 |
| mcv_2010 |
-0.1577 |
0.05897 |
| ethnicDigo |
-1.267 |
9.005 |
| ethnicDurum |
-7.427 |
3.133 |
| ethnicGiriama |
-2.598 |
0.2545 |
| ethnicJibana |
-4.254 |
1.536 |
- sickle emmean SE df lower.CL upper.CL
- NORM 5.872907 0.7893974 49 4.286553 7.459260
- HET 4.709319 1.1779876 49 2.342065 7.076574
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
g6pd_202_rtpcr _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
g6pd_202_rtpcr _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
u_rcc
g6pd_202_rtpcr _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
-254.3 |
167.3 |
-1.52 |
0.1383 |
|
| thalHET |
5.498 |
24.84 |
0.2213 |
0.8262 |
|
| thalHOM |
36.07 |
40.2 |
0.8971 |
0.3764 |
|
| mcv_2010 |
2.663 |
2.187 |
1.218 |
0.2322 |
|
| hgb_2010 |
5.802 |
11.81 |
0.4914 |
0.6265 |
|
| ethnicGiriama |
27.22 |
22.52 |
1.208 |
0.2358 |
|
| ethnicJibana |
-7.843 |
67.23 |
-0.1167 |
0.9079 |
|
| ethnicKauma |
16.05 |
54.58 |
0.2941 |
0.7706 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 40 |
64.39 |
0.1695 |
-0.01212 |
| (Intercept) |
-595.1 |
86.46 |
| thalHET |
-45.09 |
56.09 |
| thalHOM |
-45.83 |
118 |
| mcv_2010 |
-1.791 |
7.116 |
| hgb_2010 |
-18.25 |
29.85 |
| ethnicGiriama |
-18.66 |
73.1 |
| ethnicJibana |
-144.8 |
129.1 |
| ethnicKauma |
-95.12 |
127.2 |
- thal emmean SE df lower.CL upper.CL
- NORM 27.85519 27.05220 32 -27.24833 82.95872
- HET 33.35293 22.10092 32 -11.66517 78.37102
- HOM 63.92354 39.64434 32 -16.82935 144.67642

| (Intercept) |
-149.9 |
124.3 |
-1.206 |
0.2364 |
|
| sickleHET |
-0.4567 |
31.71 |
-0.0144 |
0.9886 |
|
| mcv_2010 |
1.374 |
1.722 |
0.798 |
0.4306 |
|
| hgb_2010 |
6.336 |
11.82 |
0.5362 |
0.5954 |
|
| ethnicGiriama |
29.21 |
21.97 |
1.329 |
0.1929 |
|
| ethnicJibana |
-8.41 |
72.52 |
-0.116 |
0.9084 |
|
| ethnicKauma |
-3.229 |
50.7 |
-0.0637 |
0.9496 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 40 |
64.25 |
0.1475 |
-0.007544 |
| (Intercept) |
-402.7 |
102.9 |
| sickleHET |
-64.96 |
64.05 |
| mcv_2010 |
-2.13 |
4.879 |
| hgb_2010 |
-17.71 |
30.38 |
| ethnicGiriama |
-15.5 |
73.91 |
| ethnicJibana |
-156 |
139.1 |
| ethnicKauma |
-106.4 |
99.91 |
- sickle emmean SE df lower.CL upper.CL
- NORM 33.54212 22.66274 33 -12.56557 79.64981
- HET 33.08547 30.43532 33 -28.83565 95.00660

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
g6pd_202_rtpcr _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
-4.671 |
6.045 |
-0.7727 |
0.4454 |
|
| thalHET |
-0.1367 |
0.9593 |
-0.1425 |
0.8876 |
|
| thalHOM |
0.933 |
1.557 |
0.5991 |
0.5533 |
|
| age_at_collection_years_2010 |
0.1554 |
0.1313 |
1.184 |
0.2452 |
|
| mcv_2010 |
0.05994 |
0.07711 |
0.7774 |
0.4427 |
|
| ethnicGiriama |
0.5956 |
0.8666 |
0.6872 |
0.4969 |
|
| ethnicJibana |
-0.435 |
2.473 |
-0.1759 |
0.8615 |
|
| ethnicKauma |
-0.09223 |
2.041 |
-0.04519 |
0.9642 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 40 |
2.369 |
0.1549 |
-0.02993 |
| (Intercept) |
-16.98 |
7.642 |
| thalHET |
-2.091 |
1.817 |
| thalHOM |
-2.239 |
4.105 |
| age_at_collection_years_2010 |
-0.112 |
0.4229 |
| mcv_2010 |
-0.09712 |
0.217 |
| ethnicGiriama |
-1.17 |
2.361 |
| ethnicJibana |
-5.472 |
4.602 |
| ethnicKauma |
-4.249 |
4.065 |
- thal emmean SE df lower.CL upper.CL
- NORM 1.246420 1.0081318 32 -0.8070776 3.299917
- HET 1.109717 0.8151458 32 -0.5506808 2.770115
- HOM 2.179423 1.4887439 32 -0.8530494 5.211895

| (Intercept) |
-1.833 |
4.073 |
-0.4501 |
0.6556 |
|
| sickleHET |
0.3486 |
1.152 |
0.3025 |
0.7641 |
|
| age_at_collection_years_2010 |
0.1684 |
0.1214 |
1.387 |
0.1747 |
|
| mcv_2010 |
0.02303 |
0.05421 |
0.4249 |
0.6736 |
|
| ethnicGiriama |
0.662 |
0.8338 |
0.794 |
0.4329 |
|
| ethnicJibana |
-0.8899 |
2.666 |
-0.3337 |
0.7407 |
|
| ethnicKauma |
-0.5671 |
1.838 |
-0.3085 |
0.7596 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 40 |
2.352 |
0.1406 |
-0.01561 |
| (Intercept) |
-10.12 |
6.453 |
| sickleHET |
-1.996 |
2.693 |
| age_at_collection_years_2010 |
-0.07856 |
0.4153 |
| mcv_2010 |
-0.08725 |
0.1333 |
| ethnicGiriama |
-1.034 |
2.358 |
| ethnicJibana |
-6.315 |
4.535 |
| ethnicKauma |
-4.306 |
3.172 |
- sickle emmean SE df lower.CL upper.CL
- NORM 1.095040 0.829702 33 -0.5930011 2.783082
- HET 1.443641 1.097995 33 -0.7902468 3.677528
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
thal
u_rcc
thal _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
233 |
66.76 |
3.49 |
0.000697 |
* * * |
| g6pd_202_rtpcrHET |
-61.1 |
16.27 |
-3.755 |
0.0002784 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-144.1 |
19.96 |
-7.223 |
7.035e-11 |
* * * |
| mcv_2010 |
2.657 |
0.8514 |
3.121 |
0.0023 |
* * |
| hgb_2010 |
-20.83 |
5.364 |
-3.883 |
0.0001762 |
* * * |
| ethnicGiriama |
-25.44 |
12.43 |
-2.046 |
0.04311 |
* |
| ethnicJibana |
-14.04 |
27.12 |
-0.5178 |
0.6056 |
|
| ethnicKauma |
-8.184 |
34.57 |
-0.2367 |
0.8133 |
|
| ethnicRabai |
19.37 |
44.83 |
0.432 |
0.6666 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 119 |
61.16 |
0.4106 |
0.3678 |
| (Intercept) |
100.7 |
365.2 |
| g6pd_202_rtpcrHET |
-93.35 |
-28.86 |
| g6pd_202_rtpcrHOM/HEMI |
-183.7 |
-104.6 |
| mcv_2010 |
0.9702 |
4.345 |
| hgb_2010 |
-31.46 |
-10.2 |
| ethnicGiriama |
-50.07 |
-0.8018 |
| ethnicJibana |
-67.78 |
39.7 |
| ethnicKauma |
-76.7 |
60.33 |
| ethnicRabai |
-69.47 |
108.2 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 194.98316 12.76436 110 169.687189 220.27913
- HET 133.87935 19.15047 110 95.927622 171.83108
- HOM/HEMI 50.84587 21.69640 110 7.848689 93.84304
| NORM |
100 |
| HET |
23 |
| HOM/HEMI |
12 |

| (Intercept) |
247.8 |
82.93 |
2.989 |
0.00345 |
* * |
| sickleHET |
5.45 |
19.23 |
0.2834 |
0.7774 |
|
| mcv_2010 |
1.641 |
1.046 |
1.569 |
0.1196 |
|
| hgb_2010 |
-17.63 |
6.623 |
-2.662 |
0.008917 |
* * |
| ethnicGiriama |
-19.02 |
15.21 |
-1.251 |
0.2137 |
|
| ethnicJibana |
2.789 |
33.5 |
0.08326 |
0.9338 |
|
| ethnicKauma |
-23.8 |
42.53 |
-0.5597 |
0.5768 |
|
| ethnicRabai |
39.22 |
55.42 |
0.7077 |
0.4806 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 119 |
75.75 |
0.08768 |
0.03015 |
| (Intercept) |
83.52 |
412.2 |
| sickleHET |
-32.65 |
43.55 |
| mcv_2010 |
-0.4319 |
3.714 |
| hgb_2010 |
-30.75 |
-4.507 |
| ethnicGiriama |
-49.16 |
11.12 |
| ethnicJibana |
-63.59 |
69.17 |
| ethnicKauma |
-108.1 |
60.47 |
| ethnicRabai |
-70.6 |
149 |
- sickle emmean SE df lower.CL upper.CL
- NORM 172.8363 15.99202 111 141.1470 204.5255
- HET 178.2860 21.54168 111 135.5997 220.9723

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
thal _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
13.33 |
2.495 |
5.343 |
4.993e-07 |
* * * |
| g6pd_202_rtpcrHET |
-2.284 |
0.6802 |
-3.358 |
0.001079 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-5.248 |
0.8348 |
-6.287 |
6.716e-09 |
* * * |
| age_at_collection_years_2010 |
-0.03981 |
0.08067 |
-0.4934 |
0.6227 |
|
| mcv_2010 |
-0.06867 |
0.0337 |
-2.038 |
0.04398 |
* |
| ethnicGiriama |
-1.007 |
0.5179 |
-1.945 |
0.05429 |
|
| ethnicJibana |
-0.05986 |
1.125 |
-0.0532 |
0.9577 |
|
| ethnicKauma |
0.4286 |
1.422 |
0.3014 |
0.7637 |
|
| ethnicRabai |
0.3786 |
1.903 |
0.199 |
0.8426 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 119 |
2.546 |
0.3731 |
0.3275 |
| (Intercept) |
8.387 |
18.28 |
| g6pd_202_rtpcrHET |
-3.632 |
-0.9361 |
| g6pd_202_rtpcrHOM/HEMI |
-6.902 |
-3.593 |
| age_at_collection_years_2010 |
-0.1997 |
0.1201 |
| mcv_2010 |
-0.1355 |
-0.001884 |
| ethnicGiriama |
-2.034 |
0.01887 |
| ethnicJibana |
-2.289 |
2.17 |
| ethnicKauma |
-2.39 |
3.247 |
| ethnicRabai |
-3.392 |
4.149 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 7.626856 0.5352584 110 6.5660998 8.687613
- HET 5.342873 0.8073750 110 3.7428450 6.942901
- HOM/HEMI 2.379032 0.9152256 110 0.5652699 4.192795
| NORM |
100 |
| HET |
23 |
| HOM/HEMI |
12 |

| (Intercept) |
14.9 |
2.969 |
5.019 |
1.997e-06 |
* * * |
| sickleHET |
0.1798 |
0.7658 |
0.2347 |
0.8148 |
|
| age_at_collection_years_2010 |
0.03821 |
0.09455 |
0.4042 |
0.6869 |
|
| mcv_2010 |
-0.109 |
0.03947 |
-2.762 |
0.006728 |
* * |
| ethnicGiriama |
-0.7949 |
0.607 |
-1.309 |
0.1931 |
|
| ethnicJibana |
0.6022 |
1.329 |
0.4532 |
0.6513 |
|
| ethnicKauma |
-0.1742 |
1.674 |
-0.1041 |
0.9173 |
|
| ethnicRabai |
1.438 |
2.246 |
0.6405 |
0.5232 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 119 |
3.017 |
0.1116 |
0.05553 |
| (Intercept) |
9.016 |
20.78 |
| sickleHET |
-1.338 |
1.697 |
| age_at_collection_years_2010 |
-0.1491 |
0.2256 |
| mcv_2010 |
-0.1872 |
-0.0308 |
| ethnicGiriama |
-1.998 |
0.408 |
| ethnicJibana |
-2.031 |
3.236 |
| ethnicKauma |
-3.491 |
3.143 |
| ethnicRabai |
-3.012 |
5.889 |
- sickle emmean SE df lower.CL upper.CL
- NORM 6.885353 0.644052 111 5.609121 8.161585
- HET 7.065125 0.862482 111 5.356060 8.774191
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
thal _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
225.8 |
57.86 |
3.903 |
0.0001409 |
* * * |
| g6pd_202_rtpcrHET |
-45.19 |
11.83 |
-3.82 |
0.0001919 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-139.5 |
14.39 |
-9.695 |
1.088e-17 |
* * * |
| mcv_2010 |
1.212 |
0.8439 |
1.436 |
0.1529 |
|
| hgb_2010 |
-11.62 |
4.089 |
-2.842 |
0.005086 |
* * |
| ethnicDigo |
60.88 |
39.15 |
1.555 |
0.122 |
|
| ethnicDurum |
21.19 |
54.82 |
0.3866 |
0.6996 |
|
| ethnicGiriama |
-10 |
9.36 |
-1.069 |
0.2869 |
|
| ethnicJibana |
-18.14 |
16.25 |
-1.116 |
0.2661 |
|
| ethnicKauma |
-11.59 |
32.49 |
-0.3568 |
0.7217 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 166 |
54.08 |
0.4401 |
0.4078 |
| (Intercept) |
111.5 |
340.1 |
| g6pd_202_rtpcrHET |
-68.56 |
-21.83 |
| g6pd_202_rtpcrHOM/HEMI |
-167.9 |
-111.1 |
| mcv_2010 |
-0.4548 |
2.879 |
| hgb_2010 |
-19.7 |
-3.543 |
| ethnicDigo |
-16.46 |
138.2 |
| ethnicDurum |
-87.09 |
129.5 |
| ethnicGiriama |
-28.49 |
8.487 |
| ethnicJibana |
-50.25 |
13.97 |
| ethnicKauma |
-75.76 |
52.58 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 191.81925 13.08077 156 165.9810 217.65754
- HET 146.62627 15.54775 156 115.9150 177.33755
- HOM/HEMI 52.30165 17.33244 156 18.0651 86.53821
| NORM |
133 |
| HET |
36 |
| HOM/HEMI |
22 |

| (Intercept) |
399.8 |
69.72 |
5.734 |
4.893e-08 |
* * * |
| sickleHET |
-20.22 |
17.25 |
-1.172 |
0.2429 |
|
| mcv_2010 |
-2.485 |
0.9429 |
-2.636 |
0.009234 |
* * |
| hgb_2010 |
-5.717 |
5.05 |
-1.132 |
0.2593 |
|
| ethnicDigo |
84.23 |
49.74 |
1.694 |
0.09234 |
|
| ethnicDurum |
45.31 |
68.93 |
0.6573 |
0.512 |
|
| ethnicGiriama |
6.381 |
11.61 |
0.5495 |
0.5834 |
|
| ethnicJibana |
-0.7177 |
21.4 |
-0.03354 |
0.9733 |
|
| ethnicKauma |
-43.14 |
40.66 |
-1.061 |
0.2903 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 166 |
68.13 |
0.1058 |
0.06024 |
| (Intercept) |
262 |
537.5 |
| sickleHET |
-54.28 |
13.85 |
| mcv_2010 |
-4.348 |
-0.623 |
| hgb_2010 |
-15.69 |
4.258 |
| ethnicDigo |
-14.01 |
182.5 |
| ethnicDurum |
-90.84 |
181.5 |
| ethnicGiriama |
-16.55 |
29.32 |
| ethnicJibana |
-42.98 |
41.54 |
| ethnicKauma |
-123.5 |
37.17 |
- sickle emmean SE df lower.CL upper.CL
- NORM 166.6145 16.16128 157 134.6929 198.5361
- HET 146.3989 21.28544 157 104.3562 188.4417

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
thal _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
15.05 |
2.323 |
6.479 |
1.148e-09 |
* * * |
| g6pd_202_rtpcrHET |
-1.593 |
0.4812 |
-3.31 |
0.001161 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-5.032 |
0.5879 |
-8.559 |
1.011e-14 |
* * * |
| age_at_collection_years_2010 |
-0.1252 |
0.06198 |
-2.021 |
0.04502 |
* |
| mcv_2010 |
-0.09169 |
0.03372 |
-2.719 |
0.007291 |
* * |
| ethnicDigo |
3.038 |
1.617 |
1.879 |
0.06214 |
|
| ethnicDurum |
1.414 |
2.252 |
0.6279 |
0.531 |
|
| ethnicGiriama |
-0.02755 |
0.4002 |
-0.06884 |
0.9452 |
|
| ethnicJibana |
-0.4593 |
0.6673 |
-0.6884 |
0.4922 |
|
| ethnicKauma |
-0.481 |
1.336 |
-0.3601 |
0.7193 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 166 |
2.226 |
0.4928 |
0.4635 |
| (Intercept) |
10.46 |
19.64 |
| g6pd_202_rtpcrHET |
-2.543 |
-0.6421 |
| g6pd_202_rtpcrHOM/HEMI |
-6.193 |
-3.871 |
| age_at_collection_years_2010 |
-0.2477 |
-0.002816 |
| mcv_2010 |
-0.1583 |
-0.02508 |
| ethnicDigo |
-0.1561 |
6.232 |
| ethnicDurum |
-3.035 |
5.863 |
| ethnicGiriama |
-0.8181 |
0.763 |
| ethnicJibana |
-1.777 |
0.8587 |
| ethnicKauma |
-3.12 |
2.158 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 7.855809 0.5386022 156 6.791914 8.919703
- HET 6.263200 0.6355263 156 5.007853 7.518548
- HOM/HEMI 2.823931 0.7098068 156 1.421858 4.226003
| NORM |
133 |
| HET |
36 |
| HOM/HEMI |
22 |

| (Intercept) |
22.76 |
2.603 |
8.747 |
3.208e-15 |
* * * |
| sickleHET |
-0.657 |
0.6831 |
-0.9618 |
0.3376 |
|
| age_at_collection_years_2010 |
-0.08536 |
0.07478 |
-1.142 |
0.2554 |
|
| mcv_2010 |
-0.2155 |
0.03689 |
-5.842 |
2.882e-08 |
* * * |
| ethnicDigo |
3.603 |
1.966 |
1.833 |
0.06874 |
|
| ethnicDurum |
2.099 |
2.718 |
0.7724 |
0.441 |
|
| ethnicGiriama |
0.4607 |
0.4788 |
0.9623 |
0.3374 |
|
| ethnicJibana |
0.06098 |
0.8424 |
0.07239 |
0.9424 |
|
| ethnicKauma |
-1.555 |
1.604 |
-0.9696 |
0.3337 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 166 |
2.689 |
0.2553 |
0.2174 |
| (Intercept) |
17.62 |
27.9 |
| sickleHET |
-2.006 |
0.6922 |
| age_at_collection_years_2010 |
-0.2331 |
0.06234 |
| mcv_2010 |
-0.2884 |
-0.1426 |
| ethnicDigo |
-0.2801 |
7.486 |
| ethnicDurum |
-3.269 |
7.467 |
| ethnicGiriama |
-0.485 |
1.406 |
| ethnicJibana |
-1.603 |
1.725 |
| ethnicKauma |
-4.722 |
1.613 |
- sickle emmean SE df lower.CL upper.CL
- NORM 6.910751 0.6374058 157 5.651755 8.169748
- HET 6.253713 0.8405021 157 4.593563 7.913864
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
thal _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
416 |
87.3 |
4.765 |
1.125e-05 |
* * * |
| g6pd_202_rtpcrHET |
-58.57 |
18.9 |
-3.099 |
0.002883 |
* * |
| g6pd_202_rtpcrHOM/HEMI |
-146 |
19.27 |
-7.577 |
1.799e-10 |
* * * |
| mcv_2010 |
-0.6306 |
1.491 |
-0.4228 |
0.6738 |
|
| hgb_2010 |
-16.15 |
7.166 |
-2.254 |
0.02765 |
* |
| ethnicDurum |
-70.39 |
53.16 |
-1.324 |
0.1902 |
|
| ethnicGiriama |
-35.95 |
12.98 |
-2.77 |
0.007329 |
* * |
| ethnicJibana |
-40.26 |
30.88 |
-1.304 |
0.197 |
|
| ethnicKambe |
-23.02 |
36.92 |
-0.6236 |
0.5351 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 73 |
48.34 |
0.5536 |
0.4977 |
| (Intercept) |
241.6 |
590.4 |
| g6pd_202_rtpcrHET |
-96.32 |
-20.82 |
| g6pd_202_rtpcrHOM/HEMI |
-184.5 |
-107.5 |
| mcv_2010 |
-3.61 |
2.349 |
| hgb_2010 |
-30.46 |
-1.834 |
| ethnicDurum |
-176.6 |
35.81 |
| ethnicGiriama |
-61.88 |
-10.02 |
| ethnicJibana |
-101.9 |
21.43 |
| ethnicKambe |
-96.79 |
50.74 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 163.6202 14.33377 64 134.98523 192.25522
- HET 105.0510 19.32096 64 66.45291 143.64903
- HOM/HEMI 17.6106 22.49249 64 -27.32332 62.54451
| NORM |
69 |
| HET |
10 |
| HOM/HEMI |
8 |

| (Intercept) |
485.5 |
115.9 |
4.188 |
8.653e-05 |
* * * |
| sickleHET |
49.68 |
23.96 |
2.074 |
0.04207 |
* |
| mcv_2010 |
-4.727 |
1.955 |
-2.418 |
0.01842 |
* |
| hgb_2010 |
-1.128 |
9.444 |
-0.1195 |
0.9053 |
|
| ethnicDurum |
-69.35 |
67.78 |
-1.023 |
0.3101 |
|
| ethnicGiriama |
-24.21 |
17.36 |
-1.395 |
0.1679 |
|
| ethnicJibana |
-16.65 |
41.4 |
-0.4021 |
0.6889 |
|
| ethnicKambe |
-47.51 |
53.62 |
-0.886 |
0.3789 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 73 |
64.9 |
0.1828 |
0.09479 |
| (Intercept) |
254 |
717.1 |
| sickleHET |
1.835 |
97.52 |
| mcv_2010 |
-8.631 |
-0.8225 |
| hgb_2010 |
-19.99 |
17.73 |
| ethnicDurum |
-204.7 |
66.03 |
| ethnicGiriama |
-58.87 |
10.46 |
| ethnicJibana |
-99.34 |
66.04 |
| ethnicKambe |
-154.6 |
59.58 |
- sickle emmean SE df lower.CL upper.CL
- NORM 127.5795 19.06852 65 89.4970 165.6619
- HET 177.2593 25.15834 65 127.0146 227.5039

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
thal _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
25.96 |
4.161 |
6.239 |
3.941e-08 |
* * * |
| g6pd_202_rtpcrHET |
-2.296 |
0.9242 |
-2.485 |
0.0156 |
* |
| g6pd_202_rtpcrHOM/HEMI |
-6.068 |
0.9172 |
-6.617 |
8.713e-09 |
* * * |
| age_at_collection_years_2010 |
-0.02647 |
0.1006 |
-0.2631 |
0.7933 |
|
| mcv_2010 |
-0.2495 |
0.06104 |
-4.088 |
0.0001239 |
* * * |
| ethnicDurum |
-2.579 |
2.552 |
-1.011 |
0.316 |
|
| ethnicGiriama |
-1.726 |
0.6324 |
-2.729 |
0.008203 |
* * |
| ethnicJibana |
-1.672 |
1.511 |
-1.107 |
0.2726 |
|
| ethnicKambe |
-1.756 |
1.756 |
-1 |
0.3211 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 73 |
2.354 |
0.5642 |
0.5098 |
| (Intercept) |
17.65 |
34.27 |
| g6pd_202_rtpcrHET |
-4.143 |
-0.4499 |
| g6pd_202_rtpcrHOM/HEMI |
-7.901 |
-4.236 |
| age_at_collection_years_2010 |
-0.2274 |
0.1745 |
| mcv_2010 |
-0.3714 |
-0.1276 |
| ethnicDurum |
-7.677 |
2.519 |
| ethnicGiriama |
-2.989 |
-0.4622 |
| ethnicJibana |
-4.691 |
1.347 |
| ethnicKambe |
-5.264 |
1.752 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 7.615225 0.6989321 64 6.2189481 9.011503
- HET 5.318999 0.9410477 64 3.4390399 7.198958
- HOM/HEMI 1.546731 1.0791062 64 -0.6090315 3.702494
| NORM |
69 |
| HET |
10 |
| HOM/HEMI |
8 |

| (Intercept) |
30.67 |
5.161 |
5.943 |
1.215e-07 |
* * * |
| sickleHET |
2.216 |
1.094 |
2.026 |
0.04684 |
* |
| age_at_collection_years_2010 |
0.0365 |
0.1263 |
0.2891 |
0.7734 |
|
| mcv_2010 |
-0.3538 |
0.07582 |
-4.667 |
1.578e-05 |
* * * |
| ethnicDurum |
-3.306 |
3.056 |
-1.082 |
0.2833 |
|
| ethnicGiriama |
-1.11 |
0.7946 |
-1.397 |
0.1671 |
|
| ethnicJibana |
-0.4071 |
1.904 |
-0.2138 |
0.8313 |
|
| ethnicKambe |
-2.165 |
2.372 |
-0.9125 |
0.3649 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 73 |
2.971 |
0.2948 |
0.2188 |
| (Intercept) |
20.36 |
40.98 |
| sickleHET |
0.03186 |
4.401 |
| age_at_collection_years_2010 |
-0.2156 |
0.2886 |
| mcv_2010 |
-0.5053 |
-0.2024 |
| ethnicDurum |
-9.41 |
2.798 |
| ethnicGiriama |
-2.697 |
0.4768 |
| ethnicJibana |
-4.21 |
3.395 |
| ethnicKambe |
-6.903 |
2.573 |
- sickle emmean SE df lower.CL upper.CL
- NORM 6.063284 0.8730807 65 4.319621 7.806946
- HET 8.279586 1.1476792 65 5.987512 10.571660
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
thal _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
thal _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
sickle
u_rcc
sickle _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
241.1 |
36.56 |
6.595 |
1.958e-10 |
* * * |
| g6pd_202_rtpcrHET |
-45.38 |
9.14 |
-4.965 |
1.163e-06 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-141.2 |
10.64 |
-13.26 |
7.811e-32 |
* * * |
| mcv_2010 |
1.804 |
0.4916 |
3.668 |
0.0002893 |
* * * |
| hgb_2010 |
-16.65 |
3.18 |
-5.235 |
3.135e-07 |
* * * |
| ethnicDigo |
74.75 |
56.37 |
1.326 |
0.1858 |
|
| ethnicDurum |
-24.22 |
39.86 |
-0.6075 |
0.544 |
|
| ethnicGiriama |
-16.55 |
6.945 |
-2.384 |
0.01778 |
* |
| ethnicJibana |
3.489 |
15.56 |
0.2242 |
0.8227 |
|
| ethnicKauma |
-5.196 |
22.06 |
-0.2355 |
0.814 |
|
| ethnicRabai |
7.542 |
55.77 |
0.1352 |
0.8925 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 307 |
55.37 |
0.4149 |
0.3951 |
| (Intercept) |
169.2 |
313.1 |
| g6pd_202_rtpcrHET |
-63.37 |
-27.39 |
| g6pd_202_rtpcrHOM/HEMI |
-162.1 |
-120.2 |
| mcv_2010 |
0.836 |
2.771 |
| hgb_2010 |
-22.9 |
-10.39 |
| ethnicDigo |
-36.18 |
185.7 |
| ethnicDurum |
-102.7 |
54.23 |
| ethnicGiriama |
-30.22 |
-2.886 |
| ethnicJibana |
-27.14 |
34.12 |
| ethnicKauma |
-48.61 |
38.22 |
| ethnicRabai |
-102.2 |
117.3 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 192.20502 13.47360 296 165.68882 218.72121
- HET 146.82613 14.73115 296 117.83506 175.81720
- HOM/HEMI 51.01747 16.40202 296 18.73812 83.29682
| NORM |
260 |
| HET |
58 |
| HOM/HEMI |
36 |

| (Intercept) |
366 |
54.72 |
6.688 |
1.129e-10 |
* * * |
| thalHET |
-15.81 |
9.547 |
-1.656 |
0.09877 |
|
| thalHOM |
-38.96 |
14.04 |
-2.774 |
0.005881 |
* * |
| mcv_2010 |
-1.068 |
0.7197 |
-1.484 |
0.1388 |
|
| hgb_2010 |
-10.16 |
3.989 |
-2.546 |
0.01139 |
* |
| ethnicDigo |
77.18 |
70.33 |
1.097 |
0.2733 |
|
| ethnicDurum |
-12.99 |
49.95 |
-0.26 |
0.7951 |
|
| ethnicGiriama |
-5.453 |
8.634 |
-0.6316 |
0.5281 |
|
| ethnicJibana |
24.11 |
19.39 |
1.244 |
0.2147 |
|
| ethnicKauma |
-39.2 |
28.08 |
-1.396 |
0.1638 |
|
| ethnicRabai |
32.46 |
70.09 |
0.4632 |
0.6436 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 307 |
69.47 |
0.07918 |
0.04807 |
| (Intercept) |
258.3 |
473.7 |
| thalHET |
-34.6 |
2.978 |
| thalHOM |
-66.59 |
-11.32 |
| mcv_2010 |
-2.485 |
0.3481 |
| hgb_2010 |
-18.01 |
-2.307 |
| ethnicDigo |
-61.22 |
215.6 |
| ethnicDurum |
-111.3 |
85.32 |
| ethnicGiriama |
-22.44 |
11.54 |
| ethnicJibana |
-14.05 |
62.27 |
| ethnicKauma |
-94.46 |
16.06 |
| ethnicRabai |
-105.5 |
170.4 |
- thal emmean SE df lower.CL upper.CL
- NORM 183.2146 17.59239 296 148.5926 217.8366
- HET 167.4046 17.06330 296 133.8239 200.9854
- HOM 144.2574 19.51114 296 105.8593 182.6556

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
sickle _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
14.56 |
1.354 |
10.76 |
5.219e-23 |
* * * |
| g6pd_202_rtpcrHET |
-1.681 |
0.3885 |
-4.327 |
2.072e-05 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-5.278 |
0.4504 |
-11.72 |
2.572e-26 |
* * * |
| age_at_collection_years_2010 |
-0.08531 |
0.04505 |
-1.894 |
0.05924 |
|
| mcv_2010 |
-0.08625 |
0.01854 |
-4.651 |
4.976e-06 |
* * * |
| ethnicDigo |
4.156 |
2.388 |
1.74 |
0.08287 |
|
| ethnicDurum |
-0.3782 |
1.693 |
-0.2233 |
0.8234 |
|
| ethnicGiriama |
-0.5303 |
0.3012 |
-1.76 |
0.07938 |
|
| ethnicJibana |
0.3967 |
0.662 |
0.5992 |
0.5495 |
|
| ethnicKauma |
0.06789 |
0.9395 |
0.07226 |
0.9424 |
|
| ethnicRabai |
-0.1388 |
2.382 |
-0.0583 |
0.9535 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 307 |
2.358 |
0.4332 |
0.414 |
| (Intercept) |
11.9 |
17.23 |
| g6pd_202_rtpcrHET |
-2.446 |
-0.9165 |
| g6pd_202_rtpcrHOM/HEMI |
-6.164 |
-4.392 |
| age_at_collection_years_2010 |
-0.174 |
0.003347 |
| mcv_2010 |
-0.1227 |
-0.04976 |
| ethnicDigo |
-0.5442 |
8.856 |
| ethnicDurum |
-3.711 |
2.954 |
| ethnicGiriama |
-1.123 |
0.06254 |
| ethnicJibana |
-0.9061 |
1.7 |
| ethnicKauma |
-1.781 |
1.917 |
| ethnicRabai |
-4.826 |
4.548 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 8.035471 0.5728501 296 6.908095 9.162846
- HET 6.354346 0.6232595 296 5.127765 7.580927
- HOM/HEMI 2.757326 0.6914449 296 1.396555 4.118097
| NORM |
260 |
| HET |
58 |
| HOM/HEMI |
36 |

| (Intercept) |
21.24 |
2.104 |
10.09 |
8.903e-21 |
* * * |
| thalHET |
-0.6731 |
0.3921 |
-1.716 |
0.08713 |
|
| thalHOM |
-1.722 |
0.588 |
-2.929 |
0.00366 |
* * |
| age_at_collection_years_2010 |
-0.03529 |
0.05597 |
-0.6306 |
0.5288 |
|
| mcv_2010 |
-0.1871 |
0.02735 |
-6.84 |
4.543e-11 |
* * * |
| ethnicDigo |
3.833 |
2.847 |
1.346 |
0.1792 |
|
| ethnicDurum |
-0.2421 |
2.027 |
-0.1194 |
0.905 |
|
| ethnicGiriama |
-0.171 |
0.3581 |
-0.4776 |
0.6333 |
|
| ethnicJibana |
1.115 |
0.789 |
1.413 |
0.1588 |
|
| ethnicKauma |
-1.363 |
1.142 |
-1.193 |
0.2339 |
|
| ethnicRabai |
0.8045 |
2.86 |
0.2813 |
0.7787 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 307 |
2.828 |
0.1847 |
0.1572 |
| (Intercept) |
17.09 |
25.38 |
| thalHET |
-1.445 |
0.09866 |
| thalHOM |
-2.88 |
-0.5653 |
| age_at_collection_years_2010 |
-0.1454 |
0.07485 |
| mcv_2010 |
-0.2409 |
-0.1333 |
| ethnicDigo |
-1.77 |
9.435 |
| ethnicDurum |
-4.231 |
3.747 |
| ethnicGiriama |
-0.8758 |
0.5337 |
| ethnicJibana |
-0.438 |
2.668 |
| ethnicKauma |
-3.611 |
0.8856 |
| ethnicRabai |
-4.823 |
6.432 |
- thal emmean SE df lower.CL upper.CL
- NORM 7.699965 0.7170104 296 6.288880 9.111049
- HET 7.026887 0.6915251 296 5.665958 8.387816
- HOM 5.977545 0.7941216 296 4.414705 7.540385
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
sickle _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb
| (Intercept) |
262 |
73.6 |
3.559 |
0.0009566 |
* * * |
| g6pd_202_rtpcrHET |
-107.9 |
20.08 |
-5.373 |
3.351e-06 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-159.2 |
21.29 |
-7.478 |
3.515e-09 |
* * * |
| mcv_2010 |
2.337 |
0.9859 |
2.371 |
0.02253 |
* |
| hgb_2010 |
-18.41 |
6.349 |
-2.9 |
0.00597 |
* * |
| ethnicDigo |
-2.176 |
49.19 |
-0.04423 |
0.9649 |
|
| ethnicGiriama |
-38.75 |
15.5 |
-2.501 |
0.01647 |
* |
| ethnicJibana |
-59.35 |
21.61 |
-2.746 |
0.008912 |
* * |
| ethnicKambe |
-41.96 |
36.82 |
-1.14 |
0.2611 |
|
| ethnicRabai |
8.68 |
49.48 |
0.1754 |
0.8616 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
| 51 |
47.51 |
0.6987 |
0.6326 |
| (Intercept) |
113.3 |
410.6 |
| g6pd_202_rtpcrHET |
-148.5 |
-67.36 |
| g6pd_202_rtpcrHOM/HEMI |
-202.2 |
-116.2 |
| mcv_2010 |
0.3463 |
4.328 |
| hgb_2010 |
-31.23 |
-5.591 |
| ethnicDigo |
-101.5 |
97.16 |
| ethnicGiriama |
-70.05 |
-7.461 |
| ethnicJibana |
-103 |
-15.7 |
| ethnicKambe |
-116.3 |
32.4 |
| ethnicRabai |
-91.25 |
108.6 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 204.97211 13.80872 41 177.084844 232.8594
- HET 97.05074 21.47876 41 53.673516 140.4280
- HOM/HEMI 45.77694 23.59093 41 -1.865912 93.4198
| NORM |
42 |
| HET |
11 |
| HOM/HEMI |
6 |

| (Intercept) |
217.7 |
131.8 |
1.653 |
0.1061 |
|
| thalHET |
-10.41 |
28.66 |
-0.3632 |
0.7183 |
|
| thalHOM |
14.67 |
34.4 |
0.4266 |
0.6719 |
|
| mcv_2010 |
1.273 |
1.708 |
0.7453 |
0.4603 |
|
| hgb_2010 |
-11.05 |
10.07 |
-1.097 |
0.279 |
|
| ethnicDigo |
42.55 |
83.6 |
0.509 |
0.6135 |
|
| ethnicGiriama |
-29.02 |
26.85 |
-1.081 |
0.2861 |
|
| ethnicJibana |
-80.39 |
37.77 |
-2.128 |
0.03938 |
* |
| ethnicKambe |
-29.73 |
64.48 |
-0.461 |
0.6472 |
|
| ethnicRabai |
43.24 |
82.82 |
0.5221 |
0.6044 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
| 51 |
78.35 |
0.1804 |
0.0004776 |
| (Intercept) |
-48.36 |
483.8 |
| thalHET |
-68.3 |
47.48 |
| thalHOM |
-54.79 |
84.14 |
| mcv_2010 |
-2.177 |
4.723 |
| hgb_2010 |
-31.38 |
9.289 |
| ethnicDigo |
-126.3 |
211.4 |
| ethnicGiriama |
-83.24 |
25.2 |
| ethnicJibana |
-156.7 |
-4.105 |
| ethnicKambe |
-159.9 |
100.5 |
| ethnicRabai |
-124 |
210.5 |
- thal emmean SE df lower.CL upper.CL
- NORM 178.5513 26.91957 41 124.1862 232.9165
- HET 168.1396 27.20583 41 113.1963 223.0829
- HOM 193.2261 32.85289 41 126.8784 259.5739

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb
u_ghb3
sickle _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc
| (Intercept) |
16.82 |
3.036 |
5.539 |
1.952e-06 |
* * * |
| g6pd_202_rtpcrHET |
-3.807 |
0.958 |
-3.974 |
0.0002797 |
* * * |
| g6pd_202_rtpcrHOM/HEMI |
-6.152 |
1.013 |
-6.075 |
3.382e-07 |
* * * |
| age_at_collection_years_2010 |
0.03552 |
0.1182 |
0.3006 |
0.7652 |
|
| mcv_2010 |
-0.1052 |
0.04452 |
-2.363 |
0.02293 |
* |
| ethnicDigo |
-0.8956 |
2.391 |
-0.3746 |
0.7099 |
|
| ethnicGiriama |
-1.645 |
0.7463 |
-2.204 |
0.0332 |
* |
| ethnicJibana |
-2.111 |
1.025 |
-2.06 |
0.04583 |
* |
| ethnicKambe |
-2.871 |
1.719 |
-1.67 |
0.1026 |
|
| ethnicRabai |
-0.0149 |
2.434 |
-0.006119 |
0.9951 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
| 51 |
2.252 |
0.647 |
0.5696 |
| (Intercept) |
10.69 |
22.95 |
| g6pd_202_rtpcrHET |
-5.742 |
-1.872 |
| g6pd_202_rtpcrHOM/HEMI |
-8.197 |
-4.107 |
| age_at_collection_years_2010 |
-0.2031 |
0.2741 |
| mcv_2010 |
-0.1951 |
-0.01531 |
| ethnicDigo |
-5.724 |
3.933 |
| ethnicGiriama |
-3.152 |
-0.1376 |
| ethnicJibana |
-4.181 |
-0.04097 |
| ethnicKambe |
-6.343 |
0.6018 |
| ethnicRabai |
-4.931 |
4.902 |
- g6pd_202_rtpcr emmean SE df lower.CL upper.CL
- NORM 8.109038 0.6459338 41 6.804548 9.413527
- HET 4.302008 1.0371715 41 2.207397 6.396618
- HOM/HEMI 1.957373 1.1147385 41 -0.293887 4.208633
| NORM |
42 |
| HET |
11 |
| HOM/HEMI |
6 |

| (Intercept) |
18.22 |
4.983 |
3.657 |
0.0007197 |
* * * |
| thalHET |
-0.7394 |
1.209 |
-0.6115 |
0.5443 |
|
| thalHOM |
0.107 |
1.437 |
0.07446 |
0.941 |
|
| age_at_collection_years_2010 |
0.2024 |
0.1663 |
1.217 |
0.2307 |
|
| mcv_2010 |
-0.1583 |
0.06999 |
-2.262 |
0.02907 |
* |
| ethnicDigo |
0.2429 |
3.507 |
0.06927 |
0.9451 |
|
| ethnicGiriama |
-1.397 |
1.115 |
-1.252 |
0.2176 |
|
| ethnicJibana |
-2.794 |
1.554 |
-1.798 |
0.07956 |
|
| ethnicKambe |
-2.188 |
2.61 |
-0.8382 |
0.4068 |
|
| ethnicRabai |
1.899 |
3.486 |
0.5448 |
0.5889 |
|
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
| 51 |
3.232 |
0.2734 |
0.1139 |
| (Intercept) |
8.159 |
28.29 |
| thalHET |
-3.182 |
1.703 |
| thalHOM |
-2.794 |
3.008 |
| age_at_collection_years_2010 |
-0.1335 |
0.5382 |
| mcv_2010 |
-0.2996 |
-0.01696 |
| ethnicDigo |
-6.84 |
7.325 |
| ethnicGiriama |
-3.649 |
0.856 |
| ethnicJibana |
-5.932 |
0.3444 |
| ethnicKambe |
-7.459 |
3.083 |
| ethnicRabai |
-5.141 |
8.939 |
- thal emmean SE df lower.CL upper.CL
- NORM 7.443893 1.113924 41 5.194278 9.693508
- HET 6.704445 1.123098 41 4.436304 8.972587
- HOM 7.550867 1.352847 41 4.818739 10.282997
END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY
u_rcc
sickle _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
sickle _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
u_rcc
sickle _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
sickle _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels
g6pd202 _ NORM ________________________________________________________________
g6pd202 _ HET ________________________________________________________________
g6pd202 _ HOM ________________________________________________________________
g6pd202 _ HOM/HEMI ________________________________________________________________
##wambua G6PDd heterozygotes; not accounting for sex as G6PDd is x-linked {.tabset}
##wambua G6PDd heterozygotes; not accounting for sex as G6PDd is x-linked : G6PD activity